Systems and methods for automating content design transformations based on user preference and activity data

ABSTRACT

A method includes determining a plurality of harvest content items. The harvest content items are ranked based on a performance metric. Matching criterion aspects of the harvest content items are determined. Aspects of a candidate content item are compared with the plurality of harvest content items according to the matching criterion aspects. A subset of the harvest content items that are similar to the candidate content item is determined. A transformation for the candidate content item is selected and applied to the candidate content item to generate a transformed content item.

CROSS-REFERENCE TO RELATED PATENT APPLICATION

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/537,428, titled “Systems and Methods for Automating ContentDesign Transformations Based on User Preference and Activity Data,”filed on Jul. 26, 2017, which is hereby incorporated by reference in itsentirety.

BACKGROUND

Many people use the internet every day. Some use it to discoverinformation such as news, recipes, phone numbers, etc. Some use theinternet to communicate with others through mediums such as chat rooms,message boards, and e-mail. Traffic on the internet is large and manypeople use the internet for extended amounts of time.

Users of the internet may also use the internet to such a degree thatadvertisers can effectively market goods and services to customers orpotential customers using the internet. For example, a host oradministrator of a website may place advertisements on popular pages oftheir website. Such advertisements may be related to other parts of thewebsite or goods that can be purchased that are related to the website.In another example, such advertisements can be unrelated to the website.For example, the website host or administrator may sell space toadvertise on and within the website to third parties, much like abillboard might sell or lease ad space to third parties who would likepassersby to see the advertisement.

SUMMARY

At least one aspect of this disclosure is directed to a system forautomatically transforming a content item. The system can include acontent item harvesting module configured to receive a plurality ofharvest content items. The system can include a performance metricranking engine configured to rank each of the plurality of harvestcontent items based on at least one performance metric. The system caninclude a matching criterion manager configured to determine matchingcriterion aspects of the plurality of harvest content items. Thematching criterion manager can also be configured to compare aspects ofa candidate content item with the plurality of harvest content itemsaccording to the determined matching criterion aspects. The matchingcriterion manager can also be configured to determine a subset of theplurality of harvest content items that are relevant to the candidatecontent item based on the comparison of the aspects of the candidatecontent item with the plurality of harvest content items. The system caninclude a recommendation module configured to select a firsttransformation for the candidate content item. The first transformationcan be selected to make at least one characteristic of the candidatecontent item more like the at least one characteristic of a firstharvest content item of the subset of the plurality of harvest contentitems that is ranked more highly than a second harvest content item ofthe subset of the plurality of harvest content items.

In some implementations, the system can also include a contenttransformation module configured to apply the selected firsttransformation to the candidate content item to generate a transformedcontent item. In some implementations, the content transformation modulecan be further configured to determine an intensity associated with theselected transformation prior to applying the selected transformation tothe candidate content item to generate the transformed content item.

In some implementations, the candidate content item can be an imageincluding a plurality of layers and the content transformation modulecan be further configured to apply the selected transformation byaltering a subset of the plurality of layers. In some implementations,the candidate content item can be a video including a plurality offrames and the content transformation module can be further configuredto apply the selected transformation by altering a subset of theplurality of frames.

In some implementations, the system can also include a performancemetric predictor configured to calculate a score for the candidatecontent item, based on the at least one performance metric and at leastone target audience. In some implementations, the at least oneperformance metric can be a uniqueness performance metric. In someimplementations, the performance metric predictor can be furtherconfigured to calculate a second score for the candidate content item,based on at least a second target audience. In some implementations, therecommendation module can be further configured to select the firsttransformation such that, upon application of the first transformationto the candidate content item to generate a first transformed contentitem, a score of the first transformed content item is improved relativeto the score of the candidate content item. In some implementations, therecommendation module is further configured to generate a recommendationof an alternate content item having a score that is higher than thescore of the candidate content item.

In some implementations, the system can also include a performancemeasurement module configured to determine a response of the targetaudience to the first transformed content item, subsequent to the firsttransformed content item being published. In some implementations, therecommendation module can be further configured to generate arecommendation of a second target audience for the transformed contentitem, the second target audience different from the first targetaudience.

In some implementations, the performance metric predictor can be furtherconfigured to determine an updated score for the first transformedcontent item, based on the determined response of the target audience tothe first transformed content item. The recommendation module can befurther configured to select a second transformation such that, uponapplication of the second transformation to the candidate content itemby the content transformation module to generate a second transformedcontent item, a score of the second transformed content item is improvedrelative to the updated score of the first transformed content item. Insome implementations, the recommendation module can be furtherconfigured to select the second transformation based at least in part onpreference information received from a user associated with thecandidate content item. In some implementations, the system can alsoinclude a user alert module configured to provide a message to a userassociated with the candidate content item, the message including arecommendation to apply the second transformation to the candidatecontent item.

In some implementations, the recommendation module can be furtherconfigured to select a second transformation for the candidate contentitem. The second transformation can be different from the firsttransformation. In some implementations, the recommendation module canbe further configured to provide, to a user associated with thecandidate content item, an indication of the first transformation andthe second transformation. In some implementations, the system can alsoinclude a graphical user interface (GUI) generation module configured toprovide a GUI for display on a computing device of the user. Theindication of the first transformation and the second transformation canbe provided via the GUI displayed on the user computing device.

In some implementations, the GUI generation module can be furtherconfigured to provide the GUI via at least one of an extension of a webbrowser executing on the user computing device and a client applicationexecuting on the client computing device. In some implementations, thecontent transformation module can be further configured to receive, fromthe user, a user input corresponding to a selection of at least one ofthe first transformation and the second transformation. The contenttransformation module also can be configured to apply at least one ofthe first transformation and the second transformation to the candidatecontent item to generate a transformed content item, based on the userinput. The content transformation module also can be configured toreturn the transformed content item to the user.

In some implementations, the content item harvesting module can beconfigured to receive the plurality of harvest content items based on atleast one target audience. In some implementations, at least one of thematching criterion aspects comprises a content category.

Another aspect of this disclosure is directed to a system for evaluatinga content item. The system can include a content item harvesting moduleconfigured to receive a plurality of harvest content items. The systemcan include a performance metric ranking engine configured to rank eachof the plurality of harvest content items based on at least oneperformance metric. The system can include a matching criterion managerconfigured to determine matching criterion aspects of the plurality ofharvest content items. The matching criterion manager also can beconfigured to compare aspects of a first candidate content item with theplurality of harvest content items according to the determined matchingcriterion aspects. The matching criterion manager also can be configuredto determine a subset of the plurality of harvest content items that arerelevant to the first candidate content item based on the comparison ofthe aspects of the first candidate content item with the plurality ofharvest content items. The system can include a performance metricpredictor configured to calculate a score for the first candidatecontent item, based on the at least one performance metric and at leastone target audience. The system can include a recommendation moduleconfigured to identify at least one characteristic of the firstcandidate content item and to determine whether the identifiedcharacteristic of the first candidate content item is associated with anincrease or a decrease in the score for the first candidate contentitem.

In some implementations, the system can also include a user alert moduleconfigured to provide a message to a user associated with the firstcandidate content item, the message including information correspondingto the determination of whether the identified characteristic of thefirst candidate content item is associated with an increase or adecrease in the score for the first candidate content item. In someimplementations, the content item harvesting module can be furtherconfigured to receive at least a second candidate content item from theuser. The performance metric predictor can be further configured tocalculate a score for the second candidate content item and to rank thefirst candidate content item and the second candidate content itemaccording to their respective scores.

In some implementations, the performance metric predictor can be furtherconfigured to calculate the score for the second candidate content itembased on the at least one performance metric and the at least one targetaudience. In some implementations, the user alert module can be furtherconfigured to provide a second message to the user. The second messagecan include information corresponding to the ranking of the firstcandidate content item and the second candidate content item. In someimplementations, the at least one performance metric can include auniqueness performance metric.

Another aspect of this disclosure is directed to a method forautomatically transforming a content item. The method can includereceiving, by a content item harvesting module, a plurality of harvestcontent items. The method can include ranking, by a performance metricranking engine, each of the plurality of harvest content items based onat least one performance metric. The method can include determining, bya matching criterion manager, matching criterion aspects of theplurality of harvest content items. The method can include comparing, bythe matching criterion manager, aspects of a candidate content item withthe plurality of harvest content items according to the determinedmatching criterion aspects. The method can include determining, by thematching criterion manager, a subset of the plurality of harvest contentitems that are relevant to the candidate content item based on thecomparison of the aspects of the candidate content item with theplurality of harvest content items. The method can include selecting, bya recommendation module, a first transformation for the candidatecontent item. The first transformation can be selected to make at leastone characteristic of the candidate content item more like the at leastone characteristic of a first harvest content item of the subset of theplurality of harvest content items that is ranked more highly than asecond harvest content item of the subset of the plurality of harvestcontent items.

In some implementations, the method can also include applying, by acontent transformation module, the selected first transformation to thecandidate content item to generate a transformed content item. In someimplementations, the method can also include determining, by the contenttransformation module, an intensity associated with the selectedtransformation prior to applying the selected transformation to thecandidate content item to generate the transformed content item.

In some implementations, the method can also include calculating, by aperformance metric predictor, a score for the candidate content item,based on the at least one performance metric and at least one targetaudience. In some implementations, the method can also includeselecting, by the recommendation module, the first transformation suchthat, upon application of the first transformation to the candidatecontent item to generate a first transformed content item, a score ofthe first transformed content item is improved relative to the score ofthe candidate content item. In some implementations, the method can alsoinclude generating, by the content transformation module, arecommendation of an alternate content item having a score that ishigher than the score of the candidate content item.

In some implementations, the method can also include determining, by aperformance measurement module, a response of the target audience to thefirst transformed content item, subsequent to the first transformedcontent item being published. In some implementations, the method canalso include generating, by the recommendation module, a recommendationof a second target audience for the candidate content item, the secondtarget audience different from the first target audience.

In some implementations, the method can also include determining, by theperformance metric predictor, an updated score for the first transformedcontent item, based on the determined response of the target audience tothe first transformed content item. The method can also includeselecting, by the recommendation module, a second transformation suchthat, upon application of the second transformation to the candidatecontent item by the content transformation module to generate a secondtransformed content item, a score of the second transformed content itemis improved relative to the updated score of the first transformedcontent item.

An illustrative system includes a memory and a processor coupled to thememory. The processor receives a first search criteria. The first searchcriteria specifies a first custom author crowd. The processor alsoreceives a second search criteria. The second search criteria specifiesa second custom author crowd. The processor also determines a firstfluctuation of first content generated by the first custom author crowdand determines a second fluctuation of second content generated by thesecond custom author crowd. The processor also determines a fluctuationmagnitude. The fluctuation magnitude indicates a difference between thefirst fluctuation to the second fluctuation.

An illustrative method comprising includes receiving, by a processor ofa computing device, a first search criteria. The first search criteriaspecifies a first custom author crowd. The method also includesreceiving a second search criteria. The second search criteria specifiesa second custom author crowd. The method also includes determining afirst fluctuation of first content generated by the first custom authorcrowd and determining a second fluctuation of second content generatedby the second custom author crowd. The method also includes determininga fluctuation magnitude. The fluctuation magnitude indicates adifference between the first fluctuation to the second fluctuation.

An illustrative non-transitory computer readable medium havinginstructions stored thereon that, upon execution by a computing device,cause the computing device to perform operations. The operations includereceiving a first search criteria. The first search criteria specifies afirst custom author crowd. The operations also include receiving asecond search criteria. The second search criteria specifies a secondcustom author crowd. The operations also include determining a firstfluctuation of first content generated by the first custom author crowdand determining a second fluctuation of second content generated by thesecond custom author crowd. The operations also include determining afluctuation magnitude. The fluctuation magnitude indicates a differencebetween the first fluctuation to the second fluctuation.

An illustrative system includes a memory and a processor coupled to thememory. The processor provides potential requests. Each of the potentialrequests include an indication of at least one recommended aspect typefor a future content, action, or behavior. The processor also receives aselection of at least one of the potential requests for the at least onerecommended aspect type for the future content, action, or behavior. Theselection is associated with a unique author in at least one socialnetwork, website, application software, or mobile application software(app). The processor also automatically determines, in response to theselection, the recommended aspect for the future content, action, orbehavior. The recommended aspect is determined at least in part based onactivity data that indicates aspects of other content authored by orinteracted with by a plurality of authors in the at least one socialnetwork, website, application software, or mobile application software(app) prior to receipt of the selection.

An illustrative method includes providing, by a processor of a computingdevice, potential requests. Each of the potential requests include anindication of at least one recommended aspect type for a future content,action, or behavior. The method further includes receiving, by theprocessor, a selection of at least one of the potential requests for theat least one recommended aspect type for the future content, action, orbehavior. The selection is associated with a unique author in at leastone social network, website, application software, or mobile applicationsoftware (app). The method further includes automatically determining,by the processor, in response to the selection, the recommended aspectfor the future content, action, or behavior. The recommended aspect isdetermined at least in part based on activity data that indicatesaspects of other content authored by or interacted with by a pluralityof authors in the at least one social network, website, applicationsoftware, or mobile application software (app) prior to receipt of theselection.

An illustrative non-transitory computer readable medium havinginstructions stored thereon that, upon execution by a computing device,cause the computing device to perform operations. The operations includeproviding potential requests. Each of the potential requests include anindication of at least one recommended aspect type for a future content,action, or behavior. The operations also include receiving a selectionof at least one of the potential requests for the at least onerecommended aspect type for the a future content, action, or behavior.The selection is associated with a unique author in at least one socialnetwork, website, application software, or mobile application software(app). The operations also include automatically determining, inresponse to the selection, the recommended aspect for the futurecontent, action, or behavior. The recommended aspect is determined atleast in part based on activity data that indicates aspects of othercontent authored by or interacted with by a plurality of authors in theat least one social network, website, application software, or mobileapplication software (app) prior to receipt of the selection.

An illustrative system includes a memory and a processor coupled to thememory. The processor determines a recommended aspect for futurecontent, action, or behavior. The recommended aspect is determined atleast in part based on activity data that indicates aspects of othercontent authored by or interacted with by a plurality of authors in atleast one social network, website, application software, or mobileapplication software (app) prior to receipt of the selection. Theprocessor also provides the recommended aspect for the future content,action, or behavior for a unique author in the at least one socialnetwork, website, application software, or mobile application software(app). The processor also determines whether the unique author postedthe future content, took the action, or engaged in the behavior inaccordance with the recommended aspect. The processor also determines,based at least in part on the determination of whether the unique authorposted the future content, took the action, or engaged in the behaviorin accordance with the recommended aspect, an agility rating thatindicates a responsiveness of the unique author to the recommendedaspect.

An illustrative method includes determining, by a processor of acomputing device, a recommended aspect for future content, action, orbehavior. The recommended aspect is determined at least in part based onactivity data that indicates aspects of other content authored by orinteracted with by a plurality of authors in at least one socialnetwork, website, application software, or mobile application software(app) prior to receipt of the selection. The method also includesproviding, by the processor, the recommended aspect for the futurecontent, action, or behavior for a unique author in the at least onesocial network, website, application software, or mobile applicationsoftware (app). The method also includes determining, by the processor,whether the unique author posted the future content, took the action, orengaged in the behavior in accordance with the recommended aspect. Themethod also includes determining, by the processor, based at least inpart on the determination of whether the unique author posted the futurecontent, took the action, or engaged in the behavior in accordance withthe recommended aspect, an agility rating that indicates aresponsiveness of the unique author to the recommended aspect.

An illustrative non-transitory computer readable medium havinginstructions stored thereon that, upon execution by a computing device,cause the computing device to perform operations. The operations includedetermining a recommended aspect for future content, action, orbehavior. The recommended aspect is determined at least in part based onactivity data that indicates aspects of other content authored by orinteracted with by a plurality of authors in at least one socialnetwork, website, application software, or mobile application software(app) prior to receipt of the selection. The operations also includeproviding the recommended aspect for the future content, action, orbehavior for a unique author in the at least one social network,website, application software, or mobile application software (app). Theoperations also include determining whether the unique author posted thefuture content, took the action, or engaged in the behavior inaccordance with the recommended aspect. The operations also includedetermining, based at least in part on the determination of whether theunique author posted the future content, took the action, or engaged inthe behavior in accordance with the recommended aspect, an agilityrating that indicates a responsiveness of the unique author to therecommended aspect.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative embodiments will hereafter be described with reference tothe accompanying drawings.

FIG. 1 is a block diagram illustrating computing devices and a serverthat may be used in accordance with an illustrative embodiment.

FIG. 2 is a flow diagram illustrating a method of determiningfluctuations in a social media custom author group in accordance with anillustrative embodiment.

FIG. 3 is a flow diagram illustrating a method of monitoring a socialmedia custom author group and sending an alert when fluctuations ofauthor postings reach a predetermined threshold in accordance with anillustrative embodiment.

FIG. 4 is a flow diagram illustrating a method of comparing fluctuationsin multiple social media custom author groupings in accordance with anillustrative embodiment.

FIG. 5 is a flow diagram illustrating a method of measuringeffectiveness of an engagement and/or advertisement campaign based onthe monitoring of a social media custom author grouping in accordancewith an illustrative embodiment.

FIG. 6 is a flow diagram illustrating a method of defining, monitoring,and using a custom author grouping to run a marketing campaign inaccordance with an illustrative embodiment.

FIG. 7 is a flow diagram illustrating a method of determining arecommended aspect for future content based on historical response dataof a plurality of authors in accordance with an illustrative embodiment.

FIG. 8 is a flow diagram illustrating a method of determining an agilityrating indicating a responsiveness of an author to recommended aspectsfor future content in accordance with an illustrative embodiment.

FIG. 9 is a flow diagram illustrating a method for alerting a user thatthe user's agility rating has dropped below that of a second user inaccordance with an illustrative embodiment.

FIG. 10 is a block diagram illustrating a system for transforming one ormore content items in accordance with an illustrative embodiment.

FIG. 11 is a flow diagram illustrating a method for transforming one ormore content items in accordance with an illustrative embodiment.

FIG. 12 is a flow diagram illustrating a method for evaluating one ormore content items in accordance with an illustrative embodiment.

DETAILED DESCRIPTION

Described herein are illustrative embodiments for methods and systemsthat provide for quantifying social audience activation through searchand comparison of custom author groupings. In an illustrativeembodiment, a user of the system may enter a search criteria thatspecifies a custom author crowd. The search criteria may specify variousdemographic information related to authors, posts created by authors,preferences of authors, temporal considerations (when did an author dosomething), or other various search criteria as disclosed herein. Theuser may also be able to enter multiple search criteria to specify,define, and/or search for a custom author crowd.

As disclosed herein, a user is generally referred to as a user of thedisclosed system and methods, while an author is generally referred toas any user of social media. Whether the author actually “authors” postsis irrelevant to their categorization as an author. For example, anauthor as defined here may never actually author a post, but mayinteract on social media in other ways. In short, a distinction is madein the present application between a user of the disclosed system (the“user”) and a user of social media (an “author”). The terminology usedthroughout the present application is not meant to limit the activity ofan author or user, or to prevent an author from also being a user orvice versa. Rather, the terminology is merely used to provide clarityand distinguish between users and authors. A user generally refers to aperson using the systems and methods disclosed herein, while an authorgenerally refers to a person using a website, social network,application software (apps), etc. (including applications for mobilephones, smart phones, tablets, personal data assistants (PDA's),laptops, desktop computers, etc. In other words, the system and methodsdisclosed herein may be used across one or more platforms and mediumsincluding social networks, websites, mobile phone apps, and the like.

Once a crowd has been defined by a user, that crowd can be stored,analyzed, and/or tracked for various fluctuations within the crowd basedon the authors in the crowd's behavior after the crowd has been defined.Many examples of fluctuations that may be determined by the system aredisclosed herein, and are not meant to limit the possible fluctuationsthat may be tracked, analyzed, and/or determined. In one illustrativeembodiment, a custom crowd may be initially defined by searching forauthors who have authored a social media post within the past threemonths about any type of carbonated soft drink. Such a search mayinclude search for different types and brands of carbonated soft drinksin social media posts. Whoever authored those posts would then beincluded in the custom author crowd.

A user of the system and methods disclosed herein may be differentpersons or entities. For example, a user may be an advertiser oragent/staffer of the advertiser who wishes to search for and createcustom author crowds to track effectiveness of their advertisingcampaigns. In another example, the user may be a social network oragent/staffer of the social network who wishes to search for and createcustom author crowds. A social network may wish to use custom authorcrowds for a variety of purposes. For example, the social network maywish to track their own advertising campaigns or advertising campaignsof those who use the social network to advertise. In the latter example,tracking others' advertising campaigns on the social network may allowthe social network to better promote the effectiveness of advertising ontheir social network, and thereby increase advertisement spending ontheir social network. In another example, the social network (or anagent/staffer of the network) may perform searching for and tracking ofcustom author crowds on behalf of a separate advertising entity. In thissituation, the advertiser may or may not dictate how the searching andtracking should be done by the social network. When the searching andtracking is not dictated by the advertiser, the social network may beoffering the searching and tracking services as part of advertisementservices paid for by the advertiser. In another example, the searchingand tracking may be provided by the social network to advertisers. Inthis example, the advertiser may be the user of the system. Furthermore,a social network in this example may exert some control over how thesearching and tracking is accomplished. For example, the social networkmay limit the number or type of authors the advertiser can search for.In another example, the social network may limit the number of customauthor crowds the advertiser can search for or save for tracking. Thesocial network could also limit the total number of authors searched forand or tracked by an advertiser. The social network could also limit thenumber of authors allowed in each custom author group tracked by theadvertiser.

In other examples, the system and methods disclosed herein may operateacross multiple mediums and platforms such as websites, social medianetworks, and/or mobile apps. For example, an advertiser may want todefine a custom author crowd by performing searches of Facebook™authors. The advertiser may also wish to find the same authors theyalready found on Facebook™ on another medium. Examples of other mediumsmay include a Dictionary.com™ mobile app, a user of ESPN™ FantasyFootball services, or individuals with an account on an online shoppingwebsite such as Amazon™. The advertiser may have a particular rationalefor discovering or finding users on other mediums as well. For example,the advertiser may operate the mobile app Uber™, which offers taxi-likeservices. Uber™ may wish to identify authors that use a mobile app thatallows tracking of city buses or other transportation related apps. In afurther example, Uber™ may wish to identify authors that use any sort ofroad navigation app such as Google™ Maps. One possible implementationmay be to market to those who use such navigation or transportation appswhenever there are a surplus of Uber™ drivers in a certain town or area.The system may even be able to identify when a particular app isactively being used. In this scenario, an author may be using anavigation app such as a city bus tracker app during a time when thereis a surplus of Uber™ drivers. The system may identify the authorsactively using the bus tracker app and market Uber™ to those authors.The identification could happen automatically and marketing may happenautomatically as predefined by a user. In another embodiment, theidentification of surplus drivers and potential market for those driversmay occur automatically and the marketing may be executed manually. In athird embodiment, all steps may be performed manually by a user. Inthese examples, an app developer may be able to open up their authordatabase to a broader cross-platform activation system that may betapped into by advertisers and other entities. The advertisers maytarget users on apps or platforms they do business on, or perform otherrelevant cross-platform marketing and targeting.

Next, a baseline magnitude may be determined using a fluctuationcriteria. For example, within the custom author crowd, the fluctuationcriteria may be set as root beer. In this embodiment, any author in thecustom crowd that has authored a post about root beer in the past threemonths (that is, whoever has previously posted about root beer in theset amount of time before the custom crowd is created or specified) is apart of the baseline magnitude used to calculate a fluctuation. A groupof users that are a part of the baseline magnitude may be considered tobe a part of a community that enjoys root beer within the custom authorcrowd. Fluctuation criteria may also have the same parameters as theinitial search criteria.

Once the custom author crowd has been specified and a baseline magnitudeis determined using a fluctuation criteria, the system may monitor thecustom author crowd in order to determine a fluctuation of the customauthor crowd if authors in the custom author crowd author content orengage in a behavior that is related to the fluctuation criteria. Forexample, an author that previously had not posted about root beer orpreviously been considered part of a community that enjoys root beer mayauthor an online social media posting regarding their experience tryingroot beer for the first time and enjoying it. The system may determine afluctuation in the custom author crowd based on the online social mediaposting. That is, the community of those who enjoy root beer within thecustom author crowd has fluctuated upward. In other embodiments, thesystem may determine a downward fluctuation. For example, an author mayleave an affinity group for root beer hosted by a social media website,which may indicate a downward fluctuation and that the author has leftthe community of those who enjoy root beer. In another example, a systemmay determine that an author's failure to author content about root beerover a certain time period is a downward fluctuation and that the authorhas left the community of those that enjoy root beer. In an illustrativeembodiment, the system is monitoring a plurality of authors in a customauthor crowd for overall fluctuations based on a fluctuation criteria.That is, the system can determine how many authors in the custom authorcrowd have joined and/or left a community defined by the fluctuationcriteria.

In an illustrative embodiment, multiple fluctuation criteria may be usedwith the same custom author crowd. In this example, a custom authorcrowd may be watched for fluctuations in multiple types of things. Forexample, a custom author crowd may be watched in regards to root beer asin the preceding example, and the custom author crowd may also bewatched in regards to orange juice. In this example, the additionalfluctuation criteria may also be used to establish a baseline communityof those authors in the custom author crowd who have authored contentindicating a positive emotion toward orange juice. Multiple fluctuationcriteria used for the same custom author crowd may or may not be relatedto each other. In this example, the two fluctuation criteria are relatedto each other, as both of them are beverages. Similarly, in otherembodiments, whenever there are multiple fluctuation criteria used, themultiple fluctuation criteria may be part of a common fluctuationcriteria type (e.g. beverages, as in the previous example).

In another illustrative embodiment, a user may specify more than onecustom author crowd. Multiple custom author crowds may have at least onedifferent author from each other. In some examples, different customauthor crowds may have one or more authors in common. In other examples,different custom author crowds may be mutually exclusive and not haveany authors in common. Each of the custom author crowds can be monitoredfor fluctuations based on fluctuation criteria, similar to the examplesdisclosed herein. Where different custom author crowds are monitoredbased on the same fluctuation criteria, the system can determine afluctuation for multiple custom author crowds based on that samefluctuation criteria. In an illustrative embodiment, the baselinedetermined using the fluctuation criteria, and the fluctuationsdetermined for the custom author crowds, can be compared to each other.In this way, a difference in fluctuations, called a fluctuationmagnitude difference, may be determined as between the multiple customauthor crowds. Returning to the root beer example, the multiple customauthor crowds may all be the target of an advertisement for root beer ormay receive a promotional coupon for root beer. The custom author crowdsmay then be monitored to determine how, and when, the fluctuations ofthe custom author crowds change based on the advertisement or coupon. Insome embodiments, one custom author crowd may have a differentfluctuation than another custom author crowd. The resulting fluctuationmagnitude difference in the crowds may indicate to a user the relativeeffectiveness of the advertisement or coupon on a particular customauthor crowd.

In addition to comparing multiple custom author crowds to each other totrack performance and return on investment for advertising and otherauthor engagement, a custom author crowd may also be compared to apre-defined or curated social community, following, or fan base. Inother words, a custom author crowd may be compared to another crowd thatserves as a baseline or other reference point for the custom authorcrowd. A pre-defined or curated social community may be all the authorson a social media web site or may be all the authors the system hasaccess to. A pre-defined or curated social community may also consist ofa list of current paying customers or former customers, followers or fanbases of the user's social media accounts at a given point in time,followers or fan bases of specific competitors' social media accounts orother stakeholders' social media accounts, pre-existing whitelists ofauthors who have or are thought to have certain characteristics,influencer lists, custom audiences that may have been generated,procured, targeted, or otherwise leveraged in other marketing oradvertising campaigns, or any other applicable user listing. Anotherpre-defined or curated social community may be determined similar to acustom crowd (by searching based on demographics, posts, etc.) but maybe saved in the system perpetually and thus is characterized as abaseline pre-defined social community.

Advantageously, the system provides the ability to effectivelyinterrelate paid audiences (the targets of advertising/paid/sponsoredcontent) and owned audiences (those authors who already follow acompany/product/brand account such as a Twitter™ account and are membersof the company/product/brand's community). To this end, the system canshow after an advertising campaign that more of the authors in atargeted crowd have joined the following (by following thecompany/product/brand Twitter™ account, for example). This can bereferred to as a crowd penetration metric. One by one the system canshow authors in a custom author crowd being captured. Advantageously,when an author follows a brand's Twitter™ account, the author is morelikely to see unsponsored content posted on the brand's account. This ishelpful because the unsponsored content is essentially free to post.Thus, by keeping track of how many authors the brand has captured, itcan also keep track of the relative effectiveness of their unsponsoredcontent as well.

In other illustrative embodiments, multiple custom author crowds may bemonitored for various and different fluctuation criteria as desired by auser. For example, a user may designate one fluctuation criteria asBrand A Root Beer and may designate a second fluctuation criteria asBrand B Root Beer. Both fluctuation criteria may be applied to the samecustom author crowd. Accordingly, the custom author crowd may bemonitored to determine not only how the custom author crowd isfluctuating in its sentiments toward Brand A Root Beer, but also how thecustom author crowd's sentiment is fluctuating with regard to Brand BRoot Beer. This may be useful if Brand A Root Beer and Brand B Root Beerare competitors for the same customers. Similarly, the multiplefluctuation criteria (Brand A and Brand B) may be applied to multiplecustom author crowds. Multiple custom author crowds may be selected onthe basis of demographics, behavioral tendencies, lifestyle indicators,or other specific market segmentation criteria, thus allowing a user tomonitor and compare how fluctuations regarding Brand A and Brand B rootbeers are changing in particular demographic groups or target marketsegments.

In an illustrative embodiment, the search criteria that specifies acustom author crowd may include multiple criteria of varying types. Forexample, the search criteria may include authors in the custom authorcrowd who have authored a social media post about cheese and who live inthe state of Wisconsin. In another example, the search criteria mayinclude authors in the custom author crowd who have liked a particularcelebrity (or joined an affinity group for a particular celebrity), suchas Harry Houdini, and authored a social media post about magic withinthe last 6 months. In another example, the search criteria may includeauthors in the custom author crowd who have purchased tea online in thelast year and live in or around Boston, Mass. In another example, thesearch criteria may include authors in the custom author crowd who haveauthored a post on social media about their cell phone provider and whohave authored a post on social media about their subscription to paytelevision within the last year.

In an illustrative embodiment, multiple custom author crowds may bespecified and stored utilizing systems and methods disclosed herein. Inthis embodiment, two different custom author crowds may include commonauthors. In one embodiment, no action is taken by the system withregards to the common authors. That is, the common authors are left inboth custom author crowds. In an alternative embodiment, the systemautomatically identifies that the two custom author crowds both includeat least one common author. In one embodiment, the system may present auser with a choice to remove the common author from one of the customauthor crowds. In another embodiment, the system may automaticallyremove the common author from one of the custom author crowds. Forexample, the system may automatically remove the common author from thelarger of the two custom author crowds. In another example, the systemmay automatically remove the common author from the custom author crowdthat was specified later in time as opposed to the first custom authorcrowd. In another example, the system may automatically detect when anauthor in one custom crowd joins another custom author crowd specifiedby the user. This may include custom author crowds defined by theparameters described herein, a specific social fan base, etc. It shouldalso be noted that the ability of the system to detect the presence orabsence of an author or authors in one or more crowds may not be limitedto one social networking site. In one example, the system mayautomatically detect when an author joins or leaves a crowd on socialnetworking site A and social networking site B. This may help aid theuser in making a determination that it may be more effective to targetthis author or group of authors with different messages on differentsocial platforms.

When determining a fluctuation within a custom author crowd, variousmethods and systems may be used. In an illustrative embodiment, contentgenerated by the authors that causes the system to measure a fluctuationmay include a status update. For example, an author may post a statusupdate on a social networking site. The status update may include text,image, audio file, video, symbols, and/or universal resource identifiers(URIs) that comprise a fluctuation criteria. That is, the author'sstatus update may include text or a URI the system is looking for tomeasure a fluctuation. In another embodiment, the fluctuation criteriamonitored for and measured may be an online purchase of a good orservice. Another fluctuation criteria may be signing up for an accountwith a web site or web service. Another fluctuation criteria may beselecting a URI, or selecting a URI sent to an author through amessaging service or e-mail. Another fluctuation criteria may be viewinga particular webpage, or viewing a particular webpage for a certainamount of time. Another fluctuation criteria may be authoring a socialmedia post or posts including a particular text, image, video, audiofile, symbol, or URI more than once, or any other predetermined numberof times. Another fluctuation criteria may be authoring multiple socialmedia posts that contain a particular text or URI that are related. Forexample, the fluctuation criteria may cause the system to monitor forand measure a number of authors who post about peanut butter and jelly.Another fluctuation criteria may be joining a particular affinity groupor liking a particular fan page for an item, brand, celebrity, sportsteam, interest, etc. Other potential fluctuation criteria may includefollowing another author, retweeting and/or sharing a post from anotherauthor, liking an author, commenting on the posts of other authors, orinteracting with another author who is also a member of the same customauthor crowd. Fluctuation criteria could also be an interaction with aposted or promoted post authored by the user of the system. In otherwords, if an advertiser posts sponsored content, the fluctuationcriteria may be designed to measure how a custom author crowd interactswith and based on that sponsored content. This can help inform or alertthe user to authors' subsequent activity to a user action orinteraction. Another fluctuation criteria may involve images orcharacteristics of images (including image sequences such as videos)such as a certain image, style of an image, item or product in theimage, text or signs in an image or appended to an image, person in theimage, number of people in the image, age of people in the image,geographic locations of where the image was captured, lighting levels ofthe image, whether the image was indoor or outdoor, time an image wasoriginally captured, food in an image, resolution of an image, style ofan image (i.e., selfie, landscape, panoramic, portrait, square, filtertype, video or still, etc.), duration of an image sequence or video,duration of a particular individual or object's presence in an imagesequence or video, text or hyperlinks that appear in a video or areappended to a video, or any other characteristic of an image, imagesequence, or content of an image. In using such fluctuation criteria foran image, the system may utilize photo analysis software such as facialrecognition, image recognition, metadata reading or other analysis onimages that are searched. These and other related fluctuation criteriamay also be applied to video content and/or other rich media. Otherfluctuation criteria could also take into account the user's activityover a certain time period with respect to desired fluctuation criteriain the custom author crowd. In other words, the system may enable theuser to determine the total behaviors, postings or promoted messagesthat were directed to each custom author crowd as a proportion of theuser's total outreach efforts, and the resulting viewership andinteractions made by authors with that user's content as a proportion ofthe total interactions made by the custom author crowd during a specifictime period. These fluctuation criteria may provide signals related tothe efficiency of the user's messages and strategy to reach and engageeach custom author crowd, as well as signals related the “mindshare” orbrand awareness the user possesses within a custom author crowd.

In an illustrative embodiment, the system may be configured to send outalerts based on the tracked fluctuations of custom author groups. Forexample, if a fluctuation meets a certain magnitude, an alert may besent to a user. In just one example, 10% of a custom author group maymeet the fluctuation criteria and an alert may be sent. The 10% thatmeet the fluctuation criteria may be a total of the custom author group,or may be an additional 10% beyond those in the custom author group thathad already met the fluctuation criteria (in other words were already apart of the community) when the custom author group was created.Additional alerts may be subsequently sent out when other predeterminedthresholds are met. Thresholds may be other varying numbers than theexample 10%. Additionally, discrete numbers may be used instead ofpercentages. For example, alerts may be sent out for every 1,000 authorswho meet the fluctuation criteria. Aggregations of these alerts andother real-time performance measures may be viewable to the user in thesystem.

In another illustrative embodiment, alerts may also be sent out based onfluctuation magnitude differences between multiple custom author groups.For example, the system may encourage a race between multiple customauthor groups to meet fluctuation criteria. For example, a user maydefine two custom author groups that have different authors. The twocustom author groups may be assigned to different marketing teams totarget. The same fluctuation criteria may be measured for each of thecustom author groups. In this way, the marketing teams could compete atgetting their respective custom author groups to meet the fluctuationcriteria. Alerts may be sent when custom author groups hit certainpredetermined thresholds of meeting fluctuation criteria similar to theembodiments described above. In another embodiment, alerts may be sentout when one custom author group surpasses another custom author groupin number of authors that meet the fluctuation criteria. In this way,the marketing teams or other users would know who is in the lead formarketing success and would know in real time when they had surpassedanother group. Advantageously, this may incentivize marketers or otherusers to do a better job when reaching out to, engaging, and marketingto the various authors in the custom author groups.

In another illustrative embodiment, alerts may be sent out regardingnegative fluctuations. For example, if, in a custom author crowd, apredetermined number of authors disassociate themselves with an affinitygroup, an alert may be sent to a user to indicate a negativefluctuation. Similarly, in another example, the system may sensenegative language toward a product, person, etc. in a post authored bysomeone in the custom author crowd. These alerts may be triggered byactivities of a custom author crowd that take place on multiple socialnetworking sites, websites, or apps.

In another illustrative embodiment, alerts may be sent out based ontemporal factors. For example, an alert on the progress of fluctuationcriteria for a custom author crowd may be sent out every two weeks,regardless of whether any predetermined threshold is met. In anotherembodiment, an alert may be sent out if a predetermined threshold forfluctuation is met within a certain time period. For example, if thefluctuation of a custom author crowd based on a particular fluctuationcriteria reaches 3% in one month, an alert may be sent out.

In another illustrative embodiment, the system may be configured toalert the user when certain thresholds are met in relation to his or herown outreach efforts that may or may not be directed at specific customauthor crowds. For example, the user may want to know when he hasattained 10% mindshare within a specific custom author crowd or when hehas achieved 95% awareness in a custom author crowd. In other example,the user may want to know when his organic marketing program is at peakefficiency whereby the timing and frequency of his postings elicits thebest response rate or desired fluctuation criteria within a customauthor crowd.

In another illustrative embodiment, the system may be configured toalert the user when an author of particular importance engages incertain online activities or authors a post with certain words, images,videos, audio files, symbols, and/or URIs. For example, a user may wantto know if a famous celebrity authors a post about a user's product. Inone specific example, an under the weather President of the UnitedStates may tweet positively about the efficacy of a particular brand offacial tissue. The brand manager of that particular brand of facialtissue may wish to be alerted that such a high profile individual isevacuating his or her nasal cavities upon their particular brand ofpaper handkerchiefs. The system can alert the brand manager thusly. Thebrand manager may then choose to promote such a post using the system ortake other action based on the alert stemming from the President's nowfamous nasal mucus.

Alerts and other monitoring of fluctuation criteria may also be done inreal-time or near real-time. This would allow users to immediately knowwhen thresholds for fluctuation criteria are met. In other terminology,a user may immediately be notified when a certain number of authors fromsocial media sites have been activated or join a community based ontheir authored posts or other online actions. Advantageously, alerts andother real time notifications may trigger increased advertisementspending overall, as advertisers are able to better capitalize on trendsand current states of engagement from authors. It may even be the casethat entire marketing or advertising programs are based off ofnotification to these fluctuation criteria.

In another illustrative embodiment, the fluctuation criteria may be usedto track performance or success of a rival. For example, if someonesells a particular type of electric car, they may wish to know how manyof their targeted custom author grouping is interested in other brandsof electric cars or even gasoline cars. Accordingly, a user may setfluctuation criteria related to a competitor product as well as theirown. In another embodiment, the seller of electric cars may set afluctuation criteria to monitor and track authors in the custom authorgroup who author or engage with content relating to all cars. In thisway, the seller may be able to determine a proportion of those authoringcontent about cars generally that are interested in electric cars, orare interested particularly in the seller's type of electric cars. Inthis way, users may determine subsets of custom author crowds.Advantageously, the subsets can be dynamic, as they can be set to trackthe fluctuations of the custom author crowd in any of the ways disclosedherein. In another illustrative embodiment, this subset can be treatedas a separate custom author crowd. In other words, the definition of acustom author crowd may be analogous to a fluctuation criteria. In thisway, a custom author crowd may only include, for example, any authorthat has posted something about a car within the last year. If an authororiginally is considered part of the custom author crowd, but a yeargoes by without that author having again posted something about a car,that author may be removed from the custom author crowd.

In another illustrative embodiment, the fluctuation criteria andactivation of users in a custom author crowd can be used to trigger thepublication or use of content such as advertising. For example, thesystem may automatically post an advertisement that is viewable to thecustom author crowd when that custom author crowd reaches a particularfluctuation criteria. In just one specific example, when at least 15%percent of authors in a custom author crowd have authored a post onsocial media about football, the system may automatically publish anonline advertisement to that custom author crowd for a paid televisionsubscription service that offers football programming. In anotherembodiment, the 15% threshold being met in the custom author crowd mayalso trigger advertisements for other custom author crowds aboutfootball programming, or may trigger advertisements for all authorsabout football programming. In another illustrative embodiment, theautomatically published advertisement may only be published for theauthors who have authored a post about football. The automaticallypublished advertisements may come in many various forms. Theadvertisements may be through sponsored content on a news or pseudo-newswebsite, may be native ads or editorial content on a social networkingsite or other web property, may be a standard banner advertisement, maybe recommended and sponsored content on a shopping website, may be ane-mail, may be a paper mail advertisement, may be a sponsored video, maybe a video featuring a product (product placement or subliminaladvertising), or any other type of advertising. In another embodiment,the promoted content may be a post of one of the authors. For example,if an author posts a favorable comment about the aforementioned paidtelevision subscription service that offers football programming, thatpost may be promoted. Promoting such a post may involve prioritizing thepost for other social media users and authors so that it is seen moreoften than another post.

In another embodiment, the system may not execute the paid advertisementor unsponsored posting on the user's behalf. Instead, when thefluctuation criteria are met the system may signal an opportunity orrecommend that the user engage in a certain behavior or publish contentto capitalize on the favorable conditions within the custom authorcrowd. In such an example, execution of these actions may be facilitatedby sending the fluctuation criteria and other data from the disclosedsystem into another software application or set of software applicationsvia a customizable application program interface (API). Examples ofintegrated software applications may include but are not intended to belimited to a social media management system, a social media publishingor engagement platform, a programmatic advertising platform, a real-timebidding (RTB) platform, a demand side platform (DSP), a supply sideplatform (SSP), an advertising exchange, a content management system, acommunity platform, a marketing automation system, or any other datamanagement, analysis and optimization, web, Internet, or marketingtechnology platform. In other words, the system disclosed herein may bean enabler of other functions. For example, the execution of advertisingand marketing campaigns may not be done directly via the present system.That is, it may be the case that this system leverages an API that plugsinto well-established social media management systems like HootSuite™that offer post scheduling and publishing functionality. The system mayalso send data into programmatic ad platforms. In another example, theuser could be presented with an example post or a pre-written post topublish based on the opportunity. In yet another example, the user couldauthor their own post or advertisement based on the opportunity. Inanother embodiment, the user may be able to start a process to publish apost or advertisement, but the post or advertisement may have to beapproved by another party before it is posted. For example, if the useris a marketing agency, the agency's client may approve the post oradvertisement. In another example, the post or advertisement may beapproved by the social networking website where the post oradvertisement will be published.

In another embodiment, the system may be utilized by users to supportforecasting activities. That is, the activation history of one or morecrowds with the system may be leveraged in conjunction with planningexercises of the user and/or to help predict when certain crowds orcrowd members will engage in certain behaviors or post certain types ofcontent on a particular medium. Such trend data and other variabilitymeasures may be helpful when planning campaigns that may span multipleonline platforms, or even promote offline sales. In an illustrativeembodiment, the user may want to know how many authors have beenactivated about root beer in Milwaukee, and the rate at which thisfluctuation criteria was met over the last year. The user may thenleverage this data and other measurements to predict how many authorsmay be activated at a later time to plan his or her advertisement orengagement campaigns accordingly. In another example, if the fluctuationcriteria deals with new product availability in-store, the user mayleverage this data to inform demand planning and the stocking ofmerchandise at retail stores within the most activated geographicregions. In that way, crowd activity may help optimize inventory levelsand allow the user to better react to shifts in product or servicedemand.

Advantageously, the systems and methods disclosed herein allow socialnetworks, websites, owners and operators of application software (apps),and other content publishers to monetize their user bases and monetizetheir user bases more effectively. In other words, the system and methoddisclosed herein allows a social network to easily track howadvertisement and other targeted content or actions are affecting theiruser base. Armed with the quantifiable and objective information of howwell targeted content and advertising is received and reacted to by asocial network user base, a social network can charge higher prices toadvertisers that utilize the social network for advertising or promotingcontent. A social network may also be able to charge higher prices toadvertising customers based on the set of crowd attributes specified bythe user or by the number of concurrent custom author crowds that aresearched, targeted and tracked by the user. The customization of thesystems and methods disclosed herein also offers a significantadvantage. The system creates the opportunity for the social network tocreate a new economy around their inventory, i.e. their authors, wherethe network may define new ways in which to bid up the most sought-afteror niche prospective crowds. It can be an exchange where the economy isbased on expression and action, and it may cost advertisers more toreach the best authors or crowd segments in the highest demand.

Advantageously, the system functionality described herein may helpsocial networks and other content publishers surface important new paidand organic marketing opportunities for their advertisers, as well asvaluable remarketing opportunities for advertisers to target the samecrowd again with a new message at a certain time. Furthermore, anotheradvantage of this system may be the improvement of the social networks'own user experience through better native advertising and more relevantads. The provision of these and other benefits may help attract newadvertisers or retain existing advertisers. The system may also increasethe size and frequency of ad buys, and incentivize the perpetuation ofspend among current advertising customers. Performance metrics that maybe generated by the system related to the activities of a custom authorcrowd and the user may provide deeper context around campaignengagement. Such insights, that may be both qualitative and quantitativein nature, may enrich the return on investment that a social network iscapable of demonstrating to a prospective advertiser and thuslydifferentiate that social network's ad products from those of othersocial networks. That is, a social network using the system and methodsdescribed herein may be at an advantage in securing greater advertisingspend or “share of wallet” due to the richness and effectiveness of theadvertising experience provided.

Advantageously, the system may also interrelate success within thecurated social communities and target custom author crowds of the user.In other words, the system may be able to drive and illustrate valuablesocial media community growth for the advertisers showing that he or sheis capturing the attention and hearts of more of the users he caresabout through various programs and initiatives. This advantage alsoapplies to other user lists described elsewhere in the presentapplication that may include current customers, competitors' fan bases,influencers, etc.

Another advantage of the system and method disclosed herein can beexploited by brands and brand managers, as well as by their advertisingagencies. Similar to how social networks may exploit the systems andmethods disclosed herein, brand managers and other marketers may be ableto cause maintained or increased spending in advertisements with theobjective and quantifiable information that can be provided by thesystem and methods disclosed herein. This advantage is important becauseother forms of tracking the effectiveness of advertising (such ascounting the number of clicks a banner advertisement on a web page gets)may not as accurately reflect the effectiveness of advertising. Forexample, robots may represent some of the clicks on a banneradvertisement or other promoted content and may not accurately reflectthe number of human users that select an advertisement. Furthermore, ahuman user may accidentally click a banner advertisement and may neverbe truly interested in the advertisement. The present system and methodadds more contextual information and gives quantifiable gains andreturns for social media advertising.

Another advantage of the systems and methods disclosed herein is thatthe systems and methods may be applied across multiple social networksand platforms. That is, authors may be linked across multiple socialnetworking web sites and platforms, so that any post they author orassociation they make can be attached or linked to that particularauthor. The system may compile data and authors from multiple websitesor other data sources. The system may automatically associate accountsor authors from different social media sites with each other by matchingcharacteristics of the authors or accounts, such as an e-mail or phonenumber. Other information may also be acquired that can be used by thesystem to link multiple accounts from different social networking sitestogether as one author in the presently disclosed system. In anotherembodiment, some accounts on some social networking web sites may not beeasily linked to accounts on another social networking site, and thoseaccounts may be treated as different authors. It may even be the casethat a particular custom author crowd consists of entirely differentauthors or is simply treated as a separate population of unique authorson two or more social networks. That is, an inquiry into multi-platformcrowd membership may or may not be executed by the user. Such a systemmay also help advertisers and brand managers make informed decisionsabout social networks that are more effective and cost effective ascompared to other social networks. For example, an advertiser may focusan advertising campaign on social network A and social network B. Theresults of the system and methods disclosed herein may identify thatsocial network B showed a greater return by measuring the fluctuationcriteria for the custom author crowds in social network A and socialnetwork B. Further, advertisers may be able to learn that advertising onone social network may be measured and effected through a second socialnetwork. For example, an advertiser may sponsor an article on a socialnews website, and authors may tweet about the article separately. Thepresent system allows an advertiser to capture both how many people readthe article on the social news website and how many authors tweetedabout the article on a separate social news web site.

Advantageously, the system and methods disclosed herein allow a user toensure that the audiences they are reaching are the audiences actuallytargeted by the advertising. This is important because some metrics forachieving engagement and advertising success may not accurately reflectwhether a target market is being reached. For example, a web page mayget 1,000 new likes in a month, but if 200 of those likes are fromauthors who do not reside in a country where the user does business,those 200 likes are not particularly useful or helpful to the user.

The present system and methods may also allow a user to more effectivelybenchmark and determine the total number of their target authors for agiven promotion that exist on an advertising medium relative to adefined control group or the total population of authors. That is, anadvertiser may more easily gauge their position relative to adenominator, or average score, and whether they are indeed capturinggreater shares of the total available pie. The advertiser may alsodetermine a relevant range, and scale, on which to assess theirperformance. Advertisers have increased visibility into how effectivetheir efforts are in each segment of authors they are targeting overtime on social media. Advertising effectiveness is achievable in acontext-sensitive, quantifiable way that provides market share-likeperformance indicators on social media.

The system and methods disclosed herein also advantageously exploitpeople's natural desires for competition, achievement, and closure. Byallowing users to see real-time or near real-time results and return onad spending and quantifying those results, users may feel a better senseof accomplishment, and the feedback of return on investment mayencourage even more aggressive marketing and ad spending.

In an illustrative embodiment, the system and methods disclosed hereinmay include a software platform that provides flexible and continuoussearch, refinement, and tracking of target user segments for the purposeof improving advertising effectiveness and providing a gamifiedadvertising experience on a given digital or social medium. The systemand methods may provide utility regardless of the advertiser's firm sizeor familiarity with social media, digital and social media advertising,best practices in ad targeting, and other web or social media-relatedtechnologies.

In an illustrative embodiment, a user can custom-define target segmentsor crowds of Internet authors on any given digital or social medium.That is, the user may perform digital or social market segmentation byidentifying customized groupings of authors that represent a desiredtarget market segment. Search result groupings and sizes are returnedaccording to the user's custom search and targeting criteria made via asearch interface.

In another illustrative embodiment, a user can store and refineconceptualizations of these custom-defined and generated authorgroupings on a dashboard. These crowds can be managed, edited, andconstantly updated according to data from the ongoing, or past,activities of the authors contained within these specifically-definedsegments (profile information, follower characteristics, textexpressions, other web and social behaviors, etc), as well as from othermanual actions executed by the software user.

In another illustrative embodiment, a user can engage in a gamifiedsetting when monitoring and benchmarking all activities concerning atargeted crowd of social media authors. Advertisers will gain morecontextually relevant information about author engagements with theirads and other content, and be alerted of any other desired actions madeby authors within their custom-defined crowds. As such, the advertiserwill be able to gauge how he or she is performing in a crowd relative toothers (given his or her current level of investment and activity) andhave continuous visibility into the degree of success in capturinggreater shares of the entire available pie within the frame of specificadvertising goals or key performance indicators.

In an illustrative embodiment, the system and methods provide a searchtool and interface for returning groupings of similar authors onelectronic media based on user-defined criteria in a custom searchquery. These custom-defined and retrieved groupings of authorsconstitute a unique target market segment or crowd, which is specifiedby the user of the system. A user may make a custom search query—througheither free-form text in a search bar or by selecting from availablecheck boxes—and look for unique objects and characteristics containedwithin author records on any participating advertising medium, e.g. asocial networking platform. Upon entering a custom search query, thesystem can return results of groupings of similar authors. In otherwords, the system may not return a list of every single author thatmeets the custom search query criteria. Instead, the system may returngroups of authors that are similar. For example, a user may search forauthors that have authored posts about baseball in the last two months.The system may return groupings of similar authors. In another example,the system may display and return groupings of authors based on aparticular baseball team mentioned by the authors. The system maydisplay that 300 authors mentioned Team A, 400 authors mentioned Team B,200 authors mentioned Team C, etc. In other words, the user may specifya certain market or industry, and have the search results be groupedaccording to different brands within that industry. How the authors aregrouped may be specified by the user. That is, the groupings may becustom defined. In another example, the groupings may be based on whatsocial network the author is a part of. Using the previous example, thesystem would therefore return results showing, for example, that 700authors on Facebook™ have posted about baseball in the last two months,900 authors on Twitter™ posted about baseball in the last two months,300 authors posted on Instagram™ about baseball in the last two months,etc. Other ways the authors may be grouped is how recently they postedabout the selected custom search query. For example, in the baseballexample, authors may be grouped together as those who have posted aboutbaseball within the last day, the last week, the last month, and thelast two months. In another example, the authors may be grouped by thefrequency with which they meet the selected custom search query. Forexample, authors may be grouped together who have posted about baseballin the last two months once, 2-3 times, 4-5 times, and 6 or more times.

The system can then match all of the authors in the database of thatparticular medium (or collection of media) who possess the specifiedcriteria and return these results to the user. The retrieved list ofauthors from search will be “tagged” as members of a population ofinterest, which collectively represents a crowd the user wants totarget. Targeting criteria may include keywords selection, image andvideo shares, demographic and psychographic attributes such as age,gender, geography and interests, or other behaviors and actions,historical activity, mobile device, and other metadata indexed during aspecific time period that will allow for the grouping of similarauthors. The system can also work within the constraints of pre-definedtargeting criteria offered and controlled by an advertising mediumwhereby the process of searching and grouping authors to be served anadvertisement is executed solely by the advertising medium. That is, thesystem may be used in more of a customizable, self-service fashion bythe advertiser or be implemented by the owner of the advertising medium.As an example, the system may be provided to an advertiser who canperform searching and analysis of custom author groups, or the systemcan be used by a social network to demonstrate the effectiveness of theadvertisements on their network. In other words, the system can be usedby advertisers, on behalf of advertisers, or as marketing toadvertisers.

For example, a digital marketer for a department store may want to findall authors on Twitter™ who have mentioned Beyonce Knowles and thatdepartment store in the past year, like music, and used shopping-relatedkeywords after December 1st. The advertiser may call this segment,“Beyonce Holiday Shoppers.” The logic in performing this search is thatthis population might be interested in an offer for Beyonce's new giftset that month. The user enters this search query just like in any otherengine, for fast results on segment size and the collection ofanonymized or non-anonymized authors who match the search criteria. Forthe purpose of this user's query, the retrieved author groupingrepresents the total possible market for the campaign. It provides aquantifiable denominator for determining baselines and benchmarks, andfor calculating percentage changes and other measurements over time.This is especially useful if the advertiser wishes to send anotherpromoted or unsponsored offer at a later time to this exact samepopulation of authors, or to see if they organically take some specifiedaction of interest absent any stimulus from the user. In this way, thesystem allows the advertiser to group authors in similar contexts andview them in custom categories or crowds that are meaningful to anygiven marketing or advertising program. This new crowd can thenrepresent the target audience to which the advertiser may direct apromoted ad or even unsponsored content via e.g. a social networkingsite. Although this crowd was produced by specific search criteria at afixed point in time, the social activities of authors contained withinthis crowd will change, and new data on their activities will accumulateas time goes on.

The system also provides for visualizing search results in discretegroupings based on similarities of contained records. In addition, theseauthor groupings will be visualized and labeled with characteristics, asopposed to returning a list of individual line item results like atraditional search engine would produce. In this way, a user will not beoverwhelmed by the results of numerous individual author records.Furthermore, a third party action may not affect the ranking orrelevance of search results presented to the user. Existence in agrouping is determined solely by the presence or absence of searchedattributes in author records, which is determined by author activity andcharacteristics, and available metadata in the author database.

The aforementioned search and segmentation process can take place on apurely anonymous or personally identifiable basis, or any combinationthereof, in accordance with accepted privacy regulations and standards,privacy measures taken by individual users, and the policies ofwebsites, social networking platforms, and other advertising media whopossess the user data.

Query results from a search for authors can be saved for reference andsubsequent analysis. Author lists produced from a custom search querycan be transferred onto what will be referred to as a whitelist for thepurpose of ongoing measurement and later action. In essence, storing andmonitoring of custom search results (which happen to be social mediaauthors) can be directly paired with the search functionality.

In searching and archiving the author search results, the systemeffectively can return a digitized representation of a total crowd. Itallows the user to perform accurate segment sizing and to define andbetter understand a crowd that he or she is uniquely interested in at agiven time. Author records contained within the author database thatmatch the user's search parameters can each have unique identifiers,which enable the demarcation and aggregation processes to work easilywhen subsequently using the whitelist and/or custom author grouping.Furthermore, this may allow an author to be part of a custom authorgrouping by merely associating or storing the unique identifier in acustom author grouping. This allows a custom author grouping to containunique identifiers instead of all information relating to an author.When searching for authors, the system may have no results or databaseor populated list of authors at all stored before the search. Oncesearch parameters are entered, the system searches the internet orvarious databases (such as a social media database) for the authors andpopulates the search results.

Since an advertiser's targeted crowds are dynamic in nature, the user ofthe system disclosed herein may want to have searches and groupings forcrowds—these author search results—archived and available to referenceat a later time. The system allows the user to create his or her ownpersonal query-specific “index” of author groupings that can beextracted from the results of the search. That is, the system providesthe user with the option to populate one or more custom crowds with theresults of a search query. The capability allows a user to store,archive, refine and manually adjust results returned by his or hercustom search query. This conceptualization of a crowd of target authorscan be viewed and edited in its original form at any time.

For example, a corporate communications professional at a large retailstore may want to search for and identify the crowd on Twitter™ who iscurrently talking about the retailer's recent credit card breach. Theseauthors may be a high priority for the retailer to reach with anapologetic message immediately following the incident. However, sixmonths later, the user may want to follow up and send these exact sameauthors a different message: perhaps an exclusive, early-bird offer to abrand new clothing line for the purpose of re-engaging these potentiallyunhappy consumers. That is, the system may be utilized for remarketingcampaigns to specific user groupings.

In this way, the ability to tag and store crowds will allow theadvertiser to gain greater context into the behavior of the crowd he orshe wishes to curate and nurture over time—whether it is trackingresponsiveness to a promotion or any other author behavior. Thesecrowds, in turn, can be constantly refreshed and updated as new authorsjoin Twitter™ or any other social networking site, talk about a topic ofinterest, are served advertisements, etc. What's more, the advertisercan make comparisons and gauge performance measures exclusive to his orher specifically selected crowds—rather than on the marketing channelspend holistically. With this system, the advertiser can viewperformance measures on specific crowds, specific campaigns, and thechannels they are on.

A user of the systems and methods disclosed herein can also append anaforementioned crowd with proprietary data or third-party audience datarelated to the target author base that is not produced by the system.For example, a user could upload customer relationship management (CRM)data on individual authors, which may consist of e.g. Twitter™ IDs orother unique author identifiers accumulated from other marketingprograms. This capability provides the user with flexibility to add,change, or supplement author records that share similar characteristicswith the crowd produced by system and methods disclosed herein.

A crowd may not be subject to any change to a search engine-indexingalgorithm, availability of metadata, or by third party activity, whichwould consequently affect the organic ranking and presence or absence ofindividual search result listings. Systems and methods disclosed hereinmay also not be limited by the type of item or items returned on asearch engine results page: these may include pages, documents,descriptions, links, usernames, and any other unique records matchingthe user's search query, which may be useful to archive and reference inoriginal form at a later time.

Such a system and method as disclosed herein may be used for activatingor activated crowd members. Returning to the “Beyonce Holiday Shoppers”example from above, some of the crowd members may view an article abouta bizarre scandal with the pop star and take to Twitter™ with theircommentary. The author postings are not responding to content from theuser, but are nonetheless activated to the topic of Beyonce Knowles. So,in this way, any data on reach (impressions), behavioral engagement(likes, retweets, shares, follows and unfollows, photo uploads etc.),text expressions, image or video content shares, and even sentiment dataon those mentions (positive, negative or neutrality of posts) can allfuel for social activation metrics. In various embodiments, authors maybe activated by either online or offline stimuli. These metrics canindicate a good time to market certain products, services, content, etc.

Metrics actually measured by the system with regards to its crowds canbe varied and may be calculated in different ways. Although not acomprehensive list of possible metrics, some example measurementsinclude: (1) crowd to community ratio: size of the crowd vs. size ofcurrent follower base; (2) crowd penetration: percentage of crowdmembers contained in a specific social community or following (this canalso be similarly tracked as a “crowd to community conversion rate” or a“crowd acquisition rate”); (3) crowd conversion rate: % of crowd membersthat opt into a marketing offer/total number of crowd members who sawthe offer; (4) crowd activation rate: # of crowd members that takeaction of interest/total crowd size (this can also be tracked as the“activated” community for something within a crowd); (5) crowdengagement: # of crowd members engaging with paid or organiccontent/total overall engagement (this can also be tracked in rawnumbers); (6) crowd impressions: # of crowd members to whom paid ororganic content was displayed/total overall impressions (this can alsobe tracked in raw numbers); (7) percentage change in crowd size: (# ofcrowd members at time 1−# of crowd members at time 0)/# of crowd membersat time 0; (8) multi-crowd membership: method for finding duplicaterecords by matching unique author identifiers contained within multiplecustom author crowds; (9) crowd awareness: percentage of crowd memberswho are likely to be aware of a brand, product, or service based on theactivities and postings of these authors during a certain time period.Measurements and metrics specific to one crowd may be compared to totalmeasures that may include actions taken by authors who are notrepresented in the crowd; (10) crowd fatigue: calculated by determiningthe number and frequency of advertisements delivered to a singular crowdduring a specific time period relative to other crowds (this measurementmay also be expressed in percentage or proportional terms); (11) crowdattraction: percentage of total online activity and interactions of theauthors within a custom author crowd that concerns or is directed toother members of the custom author crowd or related to the definedcharacteristics of the crowd—this may be considered a measurement of thedegree of attraction within a custom author crowd on a social platformrelative to other crowds (this may also be expressed as an evaluation ofinter-crowd communicativeness); (12) crowd focus: percentage ofbehaviors, postings, or promoted messages by the user that were directedto each custom author crowd based on the user's total online activity(this may also be expressed in relation to the user's other customauthor crowds).

A user may also use the crowd as a basis for gaining other statisticsspecific to his target audience, including: other embodiments ofawareness measurement, advertising fatigue or other embodiments of reachand frequency capping within a crowd, content relevance, affinitystrength and comparisons, word-of-mouth marketing gauges, advocacy andloyalty indicators, or any range of behaviors and expressions—evensharing activity of different media files by these authors. The systemmay also facilitate event-based alerting and quantifiable aggregationsof activities in a custom crowd. For example, when an author uses aspecific keyword or hashtag in a tweet, the user of the system may bealerted. He may also be alerted to and may be capable of viewing thetotal number of such instances that occurred with respect to his crowdover a certain time period. The user of the system could specify thesespecific events of interest when setting up a custom search monitor.

Advantageously, the system allows for ongoing measurement of a specifiedcrowd as expressed in terms of the share of total authors that exist inthat crowd. The user experience with the system may create the feelingof a challenge for the user: it is theoretically feasible to captureone's entire target segment and to verify those successes throughvarious data points, quantitatively. That is, there are a totalquantifiable number of target authors, which can be compared againstbenchmarks and current activity levels. Each step closer to the totalmay be treated as a minor victory in itself. To accomplish this, thesystem may include a user interface or dashboard-like visualization todisplay the various crowds, calculations, comparisons, current userperformance, benchmarks, comparison to benchmarks, past userperformance, competitor performance, user activity levels, userinvestment or advertisement spending, and/or comparison to otherinvestment levels by competitors or industry averages.

An illustrative embodiment may also include notification of action oractions taken by an author contained within a custom-curated crowd thatsignals fluctuations of interest in the overall performance of thatcrowd. The addition of gamification techniques to this software platformallows for the creation of thresholds through performance metrics thatcan provide an advertiser with a sense of achievement and closure—evenaddiction—with respect to his or her current level of author activityrelative to known baselines, benchmarks, and short and long-term goals.

After creating one or more crowds, a user may choose to proactivelytarget authors by engaging in any variety of direct marketing activitiesor by leveraging specific ad products offered by the advertising medium.The user may also choose to passively view the organic activity ofauthors in those segments. In either approach, the system allows theuser to monitor subsequent author activity within his or her crowd afterthe crowd was created.

The system also provides a customizable keyword and behavior-basedalerting and aggregate measuring mechanism to the user when any of theauthors included in a custom-defined crowd executes an action orbehavior of interest on any range of digital or social media. That is,only the activity of the authors specifically contained within a user'scrowd will trigger an alert and impact overall performance measurement.The system provides a huge range of possibilities regarding thebehaviors that an advertiser may be interested to track and aggregatewithin a crowd and/or to receive a direct notification. For example, anauthor could mention a specific news event or brand name, engage with apiece of unsponsored content such as a Facebook™ message, visit awebsite, click on a paid advertisement, follow a corporate Twitter™handle, or engage in any other behavior on a digital or social mediumspecified as an action of interest by the user. Recall a previousexample: a user may want to know when his or her custom-defined crowd of“Beyonce Holiday Shoppers” takes an action. In an example apart fromengagement with paid advertisements, the user may want to know when acrowd member elects to follow a certain Twitter™ account. That is, theuser is interested in each instance an author contained within thisparticular crowd takes this singular action. In this case, the systemwill determine the baseline number of users within the custom-definedcrowd who follow the Twitter™ handle identified by the user. Everysubsequent follower the user receives to that account will be similarlysearched against the index to see if the author is a tagged usercontained within the user's custom crowd. Say, for example, @jehanhamedijust followed the user's desired Twitter™ handle. If this name exists inthe custom crowd, the user will be alerted that he has captured a newmember of his target market.

The system may also display performance metrics on a custom-curatedcrowd. The number of alerts accrued for a particular event or actiontaken by authors in a custom segment may also be tracked and quantifiedinto perpetuity. In doing so, the aggregation of these events or alertswill serve as a quantifiable representation of performance in thatcustom-curated segment of Internet authors. These alerts can continueinto perpetuity until the crowd containing all tagged authors ofinterest is deleted from the software platform.

Consider, again, that the act of following a Twitter™ handle is theuser's behavior of interest. Let's say that the user's custom-curatedcrowd consists of 100 target authors and, at the present time, 10 ofthem follow him on Twitter™. Therefore, the user's current crowdpenetration (which may also be described as a variant of a “followerrate”) with this crowd is 10%. Fast-forward one week: The user postsseveral new messages through his Twitter™ account, including a sponsoredone, and sees that he has gained new followers. In this example, theuser received 10 new followers in the week. The system searches forthese authors within the user's crowd and identifies that 5 of the newfollowers are indeed contained within the crowd. As a result, crowdpenetration has now grown to 15% (15/100) in that segment. The user canview this percentage change in performance in that crowd and evencompare it to other crowds he may have defined with the system. Thisfigure may also be compared to the total number of that account'sfollowers to determine, for example, the proportion of crowd members togeneral followers at a given time.

In this way, the system allows for the creation of benchmarks and othercomparative measures to gauge, on a continuous basis, the user'sperformance in a target crowd at the present time relative to the totalperformance that is achievable in that crowd, i.e. the denominator, atthat time. By calculating percentage changes and other raw measurementsover time with respect to each of these actions, the user is able tocreate and monitor his or her own custom, market share-like performanceindicators for each author crowd he or she wishes to target.

The system may also include capabilities for benchmarking and ongoingmonitoring. The system can provide a display that acts as a dashboardmonitoring the activity of the advertiser's crowds on each advertisingmedium. This display may show any type of market share-like keyperformance indicators (KPIs), such as percentages of awareness,purchase intent, content relevance, crowd membership growth and crowdpenetration, advertising fatigue, priming indicators, degree of topic orbrand affinity, loyalty rates, crowd acquisition rates, etc. With eachof these metrics there may also be a display of an average score and ananonymous industry leader to help instill a sense of competition andencourage continued activity. The conceptualization of a leaderboard mayalso use identifiable information of top achievers. Relative rankings inachievement may be determined with respect to performance in the samecustom author crowd, a specific category of interest, within somecompetitor set, or along any other dimension that is capable of beingtracked via fluctuation criteria. One or more of the user's crowds mayalso be included in these achievement calculations. As described, thesystem can also quantify overall success rates in each custom crowd. Inthis way, the user can view success measures at a current activity levelin relation to the total possible pie at a given time. By havingvisibility into total possible achievement or relative achievement toother crowds or other advertisers, the advertiser may be incentivized toincrease spend levels until reaching 100% or whatever his or her goalmay be.

The present system and methods may advantageously help an advertiserthink—“Okay, if I am only at 40%, where do my competitors stand?” Idon't want to be out-performed; I want to own the greatest consumermindshare of the people I care about and keep moving the needle towards100%, before they do.” In that way, the system creates and fosters anarcade-like experience that gamifies advertising expenditures and theuser experience for the software user. This model can create value forall parties involved. An advertiser may use the present system insteadof something like sponsoring a TV show or a golf event where theadvertiser has less information on who actually sees and engages withhis or her campaign.

In an illustrative embodiment, multiple custom author groupings arecompiled and available for selection or display. In this embodiment,statistical summaries of each of a user's custom author groupings can bedisplayed on one web page or a set of web pages, or within the interfaceof a software product. Such a collection of one user's custom authorgroupings may be referred to as a whitelist. The user may use thatwhitelist as the sole and primary content source for analysis, allowingthe user to track and perform measurements on the behaviors,expressions, and other fluctuations of that specific group of authors(i.e. the search results) in isolation. By performing measurements onthe activities of a specifically-defined crowd, the user is able todetermine, for example, the size of a certain crowd of users ordiscussion group, as well as determine applicable audience activationand user acquisition metrics for that crowd. These measurements willhelp the user learn if his or her recent initiatives on any one or setof social media channels are positively impacting these metrics. Thesemetrics can be referenced on an ongoing basis into the future.

The user may also decide to set up new programs around a crowd that mayinclude executing targeted marketing and advertising programs via asocial network's advertising platform or any other marketingoptimization, analysis, engagement, or technology platform describedelsewhere in the present application that may specifically be directedto reach this author set. The user may set up activity-based alerts tonotify him of certain actions taken by these crowd members or changeswithin the size and makeup of the crowd overall. For example, did peoplein the custom crowd, Pepsi™ Lovers, just follow @CocaCola™ on Twitter™after they saw a promoted ad for Diet Coke™? The user can also compareand contrast other custom author groupings he or she created along anyof the same measures.

The system allows for comparison and tracking of two or more customizedcrowds against each other for the purpose of determining relativeachievement and performance. This comparative measure may be aquantification of a desired action taken by certain authors in a crowdagainst the total crowd or comparisons along any of the metricsaforementioned. For example, the user may decide to compare two customdefined crowds versus viewing one in isolation to determine relativeactivation (or author acquisition) levels with respect to the totalnumber of social media authors in a crowd or against any other controlgroup. The user may also wish to determine whether he or she was moreeffective in activating crowd A or crowd B after executing campaignsduring a certain time period. In another embodiment, the user may noteven define these comparison crowds himself with the system; there maybe potential to compare a user-defined crowd to a crowd defined by otherusers of the system or to a sample crowd or any collection of samplecrowds already provided by the system.

In an illustrative embodiment, an advertising or marketing campaignexecuted directly through or in conjunction with the system by a usermay be used to capture more customer data. For example, a promotion on asocial network may allow authors to participate in the promotion upongiving the user an e-mail address or mobile phone number to receive theoffer. This may be an effective way to acquire new information aboutauthors in the crowd which can be stored in customer relationshipmanagement (CRM) databases maintained either in the present system orseparately by the user.

Advantageously, the present system and methods allows a user to targetand analyze the activities of authors across multiple social networkssuch as Facebook™, Twitter™, Tumblr™, LinkedIn™, Pinterest™, etc. As aresult, the system also has the capability of facilitating both thetargeting and retargeting of social media users across multiple socialnetworking sites. That is, finding and serving promotional content tothe same person when he or she is on each platform. The system can thusfunction as a neutral third party platform between social networks whereneither party needs to worry about divulging valuable information to theother, yet both parties benefit from increased advertising interest. Theuser of the system can create custom, storable audience segments on athird party platform.

FIG. 1 is a block diagram illustrating computing devices 100 and 145 anda server 125 that may be used in accordance with an illustrativeembodiment. In alternative embodiments, fewer, additional, and/ordifferent components may be included in the system. In FIG. 1, there isa computing device 100, a server 125, and a computing device 145. Thecomputing device 100 includes a processor 115 that is coupled to amemory 105. The processor 115 can store and recall data and applicationsin the memory 105. The processor 115 may also display objects,applications, data, etc. on the interface/display 110. The processor 115may also receive inputs through the interface/display 110. The processor115 is also coupled to a transceiver 120. With this configuration, theprocessor 115, and subsequently the computing device 100, cancommunicate with other devices, such as the server 125 through aconnection 170.

The server 125 includes a processor 135 that is coupled to a memory 130.The processor 135 can store and recall data and applications in thememory 130. The processor 135 is also coupled to a transceiver 140. Withthis configuration, the processor 135, and subsequently the server 125,can communicate with other devices, such as the computing device 100through a connection 170.

The computing device 145 includes a processor 155 that is coupled to amemory 150. The processor 155 can store and recall data and applicationsin the memory 150. The processor 155 is also coupled to a transceiver160. The processor 155 may also display objects, applications, data,etc. on the interface/display 165. The processor 155 may also receiveinputs through the interface/display 165. With this configuration, theprocessor 155, and subsequently the computing device 145, cancommunicate with other devices, such as the server 125 through aconnection 175.

The devices shown in the illustrative embodiment may be utilized invarious ways. For example, any of the connections 170 and 175 may bevaried. Any of the connections 170 and 175 may be a hard wiredconnection. A hard wired connection may involve connecting the devicesthrough a USB (universal serial bus) port, serial port, parallel port,or other type of wired connection that can facilitate the transfer ofdata and information between a processor of a device and a secondprocessor of a second device, such as between the server 125 and thecomputing device 165. In another embodiment, any of the connections 170and 175 may be a dock where one device may plug into another device.While plugged into a dock, the client-device may also have its batteriescharged or otherwise be serviced. In other embodiments, any of theconnections 170 and 175 may be a wireless connection. These connectionsmay take the form of any sort of wireless connection, including but notlimited to Bluetooth connectivity, Wi-Fi connectivity, or anotherwireless protocol. Other possible modes of wireless communication mayinclude near-field communications, such as passive radio-frequencyidentification (RFID) and active (RFID) technologies. RFID and similarnear-field communications may allow the various devices to communicatein short range when they are placed proximate to one another. In anembodiment using near field communication, two devices may have tophysically (or very nearly) come into contact, and one or both of thedevices may sense various data such as acceleration, position,orientation, velocity, change in velocity, IP address, and other sensordata. The system can then use the various sensor data to confirm atransmission of data over the internet between the two devices. In yetanother embodiment, the devices may connect through an internet (orother network) connection. That is, any of the connections 170 and 175may represent several different computing devices and network componentsthat allow the various devices to communicate through the internet,either through a hard-wired or wireless connection. Any of theconnections 170 and 175 may also be a combination of several modes ofconnection.

To operate different embodiments of the system or programs disclosedherein, the various devices may communicate in different ways. Forexample, the computing device 100 and computing device 145 may downloadvarious software applications from the server 125 through the internet.Such software applications may allow the various devices in FIG. 1 toperform some or all of the processes and functions described herein. Inanother embodiment, the computing devices 100 and 145 may operate usinginternet browsers that can access websites that perform thefunctionality of any of the systems and methods disclosed herein. Forexample, a user of the system and methods disclosed herein may be ableto use a computer, laptop, smartphone, etc. to access web pages providedby the system. The user could perform searches for custom author groups,save custom author groups, view analysis of those custom author groups,etc. as disclosed herein using only a website with various interfacesand web pages. Additionally, the embodiments disclosed herein are notlimited to being performed only on the disclosed devices in FIG. 1. Itwill be appreciated that many various combinations of computing devicesmay execute the methods and systems disclosed herein. Examples of suchcomputing devices may include smart phones, personal computers, servers,laptop computers, tablets, blackberries, RFID enabled devices, or anycombinations of such devices.

In one embodiment, a download of a program to the computing device 100involves the processor 115 receiving data through the transceiver 120from the transceiver 140 of the server 125. The processor 115 may storethe data (like the program) in the memory 105. The processor 115 canexecute the program at any time. In other embodiments, the computingdevice 145 may download programs in a similar manner to theclient-device. In another embodiment, some aspects of a program may notbe downloaded to the computing device 100 and computing device 145. Forexample, the program may be an application that accesses additional dataor resources located in the server 125. In another example, the programmay be an internet-based application, where the program is executed by aweb browser and stored almost exclusively in the server 125. In thelatter example, only temporary files and/or a web browser may be used onthe computing device 100 or computing device 145 in order to execute theprogram, system, application, etc.

In yet another embodiment, once downloaded to the computing device 100,the program may operate in whole or in part without communication withthe server 125. In this embodiment, the computing device 100 may accessor communicate with the server 125 only when acquiring the program,system, application, etc. through the connection 170. In otherembodiments, a constant or intermittent connection 170 may exist betweenthe server 125 and the computing device 100. Where an intermittentconnection exists, the computing device 100 may only need to communicatedata to or receive data from the server 125 occasionally.

The configuration of the server 125, the computing device 100, and thecomputing device 145 is merely one physical system on which thedisclosed embodiments may be executed. Other configurations of thedevices shown may exist to practice the disclosed embodiments. Further,configurations of additional or fewer devices than the ones shown inFIG. 1 may exist to practice the disclosed embodiments. Additionally,the devices shown in FIG. 1 may be combined to allow for fewer devicesor separated where more than the four devices shown exist in a system.

In some embodiments, the devices shown in FIG. 1 may be existing devicesthat are owned or possessed by a user, author in a crowd, other author,system administrator, etc. using the embodiments disclosed herein. Insuch an embodiment, the author or user may only need to downloadsoftware (e.g., an application or ‘app’) to the existing device toexecute the various embodiments disclosed herein. In other embodiments,specialized hardware may be used by the author or user that isspecifically designed to perform or execute the various embodimentsdisclosed herein. As such, hardware may be specifically designed toprovide such capabilities.

In an illustrative embodiment, the computing device 100 is used by auser of the system and methods disclosed herein. The computing device100 may be used to search for authors, create/specify custom authorgroups, and review the results of the monitoring of those custom authorgroups. A user may further utilize the computing device 100 to implementadvertising or marketing campaigns, or interact with and otherwisecreate content for the internet that may not explicitly be advertising,or perform any other functions as disclosed herein. The computing device145 is used by an author. The author can join social networks, followTwitter™ handles, like pages, send messages and chats, receive e-mail,author online content, navigate the internet, make purchases, triggerevents, etc. or perform any other functions as disclosed herein. Theserver 125 facilitates and hosts the system and methods that aredisclosed herein. It may store for the custom author groupings,calculate and monitor those groupings, and provides the computing device100 access to the features that are disclosed throughout the presentapplication.

FIG. 2 is a flow diagram illustrating a method 200 of determiningfluctuations in a social media custom author group in accordance with anillustrative embodiment. In alternative embodiments, fewer, additional,and/or different operations may be performed. Also, the use of a flowdiagram is not meant to be limiting with respect to the order ofoperations performed. In an operation 205, the system receives searchcriteria for authors. The search criteria may be a demographic or userprofile trait of an author, a subject matter of a social media postauthored by an author, a related subject matter of a predeterminednumber of social media posts authored by an author, a group associationof an author, an affirmative activity executed through the online socialnetwork of author, or any other search criteria as discussed elsewherein the present application. The search may be performed by a userentering text on their own through an input such as a keyboard. Thesearch may also be performed by selecting parameters from a menu such asa drop down menu. For example, a user may be able to select a desiredgender, age, home state, time zone, etc. of an author from a drop downmenu. Additionally, the search could be automatically populated based onpast searches. In other words, a user may be able to save past searchesso that he or she does not have to remember the exact parameters he orshe has previously entered. This may allow the user to run the samesearch again or use the populated information to ensure that they do notrun the same search again. The user may want to tweak the search onlyslightly. In one embodiment, the search fields may be automaticallypopulated with the most recent search parameters. In another embodiment,the interface for searching may also include a button that the user canselect to clear or set to a default of the fields that are used forsearching. In yet another embodiment, the user may be able to view anews feed-like thread of recent custom author crowd searches performedby other users of the system. The user may also apply filters withcertain criteria to limit the amount of content that is played.

In an operation 210, the search criteria is used to determine a customauthor crowd. In this embodiment, the search criteria can be used toperform a search after receiving an input from the user. If the searchresults are to the user's liking, another input can be received from theuser to indicate that the user would like a custom author crowd created.The custom author crowd is created. The user may be presented with anopportunity to name the custom author crowd, making it easier toidentify who is in the crowd, why the crowd was searched or created, orsome other identification that the user specifies. The custom authorcrowd specified at 210 is saved and monitored based on fluctuationcriteria.

In an operation 215, the system determines a fluctuation of the customauthor crowd based on author posts and/or other actions. The otheractions may be a variety of author actions such as retweeting, liking,commenting, purchasing, or any other author action or interaction asdisclosed and discussed throughout the present application. These postsand actions signal when an author or number of authors has beenactivated within the crowd. Thus, fluctuation criteria specified by theuser may also be referred to as activation criteria.

FIG. 3 is a flow diagram illustrating a method 300 of monitoring asocial media custom author group and sending an alert when fluctuationsof author postings reach a predetermined threshold in accordance with anillustrative embodiment. In alternative embodiments, fewer, additional,and/or different operations may be performed. Also, the use of a flowdiagram is not meant to be limiting with respect to the order ofoperations performed. In an operation 305, the system receives searchcriteria from the user. This search criteria is used to search forauthors or groups of authors. The search criteria may be a variety ofdemographic factors or online actions or interactions performed by theauthor, as discussed at length elsewhere in the present application.Similarly, in an operation 310, the search criteria is used to determinea custom author crowd as discussed throughout the present application.

In an operation 315, the system determines how many authors in thecustom author crowd can be characterized as being a part of a community,where the parameters of the community is specified by the user of thesystem. In other words, the system is determining a magnitude or numberof authors in the custom author crowd that have met a fluctuationcriteria. For example, the fluctuation criteria may be whether an authorhas liked a sponsored page for pizza. In the operation 315, the systemdetermines how many authors in the custom author crowd already haveliked the sponsored page for pizza. In this embodiment, the searchcriteria and the fluctuation criteria are different. In an alternativeembodiment, the search criteria and the fluctuation criteria may be thesame.

In an operation 320, the system saves the custom author crowd and thenumber and identity of authors that were determined to be part of thecommunity in the operation 315. In this embodiment, all authors in thecustom author crowd and their associated profiles are saved as thecustom author crowd. In an alternative embodiment, identifiers of eachaccount are stored in a list that serves as the custom author crowd. Anyother information relating to the authors may be stored separately butcan be referred to using the author identifiers. Similarly, anidentification or identifier may be stored with the author account oridentifier that indicates whether a particular author is part of thecommunity or another social following. In this embodiment, the systemdetermines if the authors in the custom author crowd are part of onecommunity. In an alternative embodiment, the system may determine if theauthors in the custom author crowd are a part of multiple communities.For example, the system may determine whether the authors in a customauthor crowd have been activated, that is, if the authors have postedsomething indicating their interest or activation in a subject. Forexample, authors may like, follow, author, etc. a post about fashion.The system may then determine how many authors in the custom authorgroup have been activated to be interested in fashion (part of thefashion community). The system may, in addition to the more generalfashion community, determine how many of the authors in the customauthor group are part of a particular fan base. For example, the systemmay determine how many of the authors in the custom author group haveliked, followed, etc. the clothier J. Crew™. In this way, a user may beable to determine how many authors in the custom author group areactivated, and how many authors are in an owned community such as aparticular brand. The system may utilize this functionality to comparethe owned community to the broader community. In other words, the systemcould determine what proportion of the activated community is part of aparticular owned community. In this way, the user may be able to moreaccurately track their own brand (or owned community) or that of acompetitor.

In an operation 325, the custom author crowd is monitored over time todetermine whether authors in the custom author crowd meet thefluctuation criteria and are subsequently characterized as being part ofthe community. In an operation 330, an alert is sent to the user whenthe number of authors in the community has reached a predeterminedthreshold. In an alternative embodiment, the user is alerted whenever anauthor in the custom author crowd is characterized as joining thecommunity. In another alternative embodiment, the user is alerted basedon a schedule. For example, the alert may be sent once a week and updatethe user on how many authors have left, joined, or stayed in thecommunity. Other various information or statistics may be included withthe alert.

FIG. 4 is a flow diagram illustrating a method 400 of comparingfluctuations in multiple social media custom author groupings inaccordance with an illustrative embodiment. In alternative embodiments,fewer, additional, and/or different operations may be performed. Also,the use of a flow diagram is not meant to be limiting with respect tothe order of operations performed. In an operation 405, a first searchcriteria is received from a user. The first search criteria may bereceived similar to the search criteria discussed above with respect tooperation 205 of FIG. 2.

In an operation 410, the first search criteria is used to determine afirst custom author crowd. The determining of the first custom authorcrowd may be performed similar to the determining a custom author crowd210 discussed above with respect to operation 210 of FIG. 2.

In an operation 415, a second search criteria is received from a user.The first search criteria may be received similar to the search criteriadiscussed above with respect to operation 205 of FIG. 2. In an operation420, the second search criteria is used to determine a second customcrowd. The determining of the second custom author crowd may beperformed similar to the determining a custom author crowd 210 discussedabove with respect to operation 210 of FIG. 2.

In an operation 425, the system monitors the first and second customauthor crowds over time for fluctuations of authors in the first andsecond custom author crowds who over time leave or join a community,i.e. become activated. The monitoring may be similar to operation 215 ofFIG. 2 and/or operation 325 of FIG. 3. In this embodiment, thefluctuation criteria monitored for is the same for both the monitoringof the first custom author crowd and the second custom author crowd. Inan alternative embodiment, a different fluctuation criteria may be usedor specified for the first custom author crowd and the second customauthor crowd. The monitoring determines how many authors in each crowdhave joined or left the community, and at what time these events occur.

In an operation 430, the system calculates differences in fluctuationsof authors in the first and second custom author crowds joining orleaving the community. In other words, the fluctuations within the firstand second custom author crowds are compared. The differences influctuations of the first and second custom author crowds may indicatethat certain strategies or content proved more effective at getting onecrowd over the other to join the community (i.e. to become activatedwithin some specific context), to opt into a marketing offer, to jointhe fan base of specific social account, etc. In an alternativeembodiment, many other statistics, comparisons, and ratios may becalculated as disclosed herein. In another embodiment, the system maycompare and monitor more than two custom author crowds.

FIG. 5 is a flow diagram illustrating a method 500 of measuringeffectiveness of an engagement and/or advertisement campaign based onthe monitoring of a social media custom author grouping in accordancewith an illustrative embodiment. In alternative embodiments, fewer,additional, and/or different operations may be performed. Also, the useof a flow diagram is not meant to be limiting with respect to the orderof operations performed. In an operation 505, a custom author crowd isestablished. The establishment of a custom author crowd may be performedusing steps similar to operations 205 and 210 of FIG. 2. In an operation510, the system determines how many of the authors in the custom authorcrowd may be characterized as part of the community. This determiningmay be similar to operation 315 as discussed above with respect to FIG.3.

In an operation 515, an engagement and/or advertisement campaign isexecuted by or in conjunction with the system disclosed herein. This maybe running an advertisement, posting sponsored content online, sendingout print media, running a commercial, tweeting something from anofficial account, prioritizing particular content on a social networkingwebsite, retweeting a post, or any other sort of engagement oradvertisement campaign that can be executed online or offline. In thisembodiment, the user executes the engagement and/or advertisementcampaign using the present system. That is, the user may be able to postand or schedule to be posted advertisements, sponsored content,unsponsored content, etc. utilizing the system. In an alternativeembodiment, the engagement and/or advertising campaign may be executedoutside the framework of the presently disclosed system and methods.That is, the user or party related to the user may execute an engagementand/or advertisement campaign utilizing a different electronic system,or the campaign may not be online at all. For example, the user mayexecute an engagement and/or advertisement campaign on a differentwebsite, may send out paper mailers, e-mails, run a promotion in storesfor or related to a product or products, etc.

In an operation 520, the custom author crowd is monitored for one ormore fluctuation criteria. The fluctuation criteria may be interactingwith sponsored content such as commenting on, reading, or retweeting thecontent. The fluctuation criteria may also be joining an online affinitygroup or following a particular individual or brand page or account.Another monitored fluctuation criteria may be whether the authoractually authors a post regarding sponsored content, the advertisingcampaign, or the subject of the advertising campaign. Other fluctuationcriteria that may be monitored are disclosed throughout the presentapplication.

In an operation 525, the system determines metrics of how the communityand/or the custom author group changed based on the monitoredfluctuation criteria. This can inform the user how effective theengagement, marketing and/or advertising program was. The metrics may bemany various calculations as described herein. For example, the metricsmay include number of authors who joined the community during theadvertisement campaign or a set amount of time after the campaign,percentage change of authors in the custom author crowd who joined thecommunity, number of authors who joined a corporate fan base or acelebrity brand endorser's following, etc.

FIG. 6 is a flow diagram illustrating a method 600 of defining,monitoring, and using a custom author grouping to run a marketingcampaign in accordance with an illustrative embodiment. In alternativeembodiments, fewer, additional, and/or different operations may beperformed. Also, the use of a flow diagram is not meant to be limitingwith respect to the order of operations performed. In an operation 605,the user defines the parameters for an author search. In an operation610, the search for authors is executed based on the parameters definedby the user.

In an operation 615, the authors that match the search parameters arelocated and grouped together in a custom author crowd. In an operation620, the group of authors, or crowd, are added to an editable whitelist.The whitelist, as described elsewhere in the present application, is alist (including an interface) where different custom author crowds canbe viewed, sorted, configured, deleted, etc.

In an operation 625, the whitelist (and subsequently the custom authorcrowds stored therein) is monitored to collect data and fluctuations inthe crowds based on fluctuation criteria determined by the user and/orpreset in the system. In an operation 630, custom, event-based alertsare defined by the user. For example, the user may wish to receive analert whenever an author leaves his or her community. In anotherexample, the user may wish to receive an alert whenever a particularfluctuation criteria changes such that when a predetermined threshold(which can be set by the user) is reached the alert is sent. In anotherexample, the user may wish to get an alert if the fluctuation criteriafor one custom author crowd is a higher value than the fluctuationcriteria for a second custom author crowd. Other alerts may be set anddefined, such as other alerts as defined elsewhere in the presentdisclosure.

In an operation 635, an alert is sent to the user indicating that anevent has occurred. Stated another way, the system has determined that aparticular fluctuation criteria is met as defined by the user, so analert is automatically generated and sent to the user by the system. Thealert may be sent in varying ways. For example, the alert may be sent byinstant message, short message service (SMS) text, e-mail, tweet, fax,message system viewable in the presently disclosed system interface,etc.

In an operation 640, custom author crowds are compared to other customauthor crowds in the whitelist. These comparisons may be done usingvarying methods as disclosed throughout the present disclosure.Additionally, the comparison may include calculations of particularmetrics or indicators based on the comparisons.

In an operation 645, the user can specify and execute a new marketingprogram based on alerts received and/or additional information displayedby the system. In other words, the user can start an advertisingcampaign based on an alert received. The user may also wish to accessthe features of the full system and may view additional information thatinforms their decision of whether to initiate an advertisement campaignand what type of campaign that might be. Additionally, the user may havepre-programmed one or more advertising campaigns into the system oranother adjoining system or systems that could be accessed by the systemdisclosed herein. In this embodiment the user can then just select thatadvertising campaign to commence as soon as the user receives an alert.In an alternative embodiment, an advertisement campaign may be initiatedby the system automatically when a certain alert condition exists. Inthis embodiment, the alert sent to the user may include the alertinformation as well as a notification that the advertisement campaignwas initiated automatically. In another embodiment, the execution of anadvertisement campaign may be facilitated via an application programminginterface (API) that may allow the user to integrate intelligencegenerated and provided by this system into other cross-channeladvertising strategies and initiatives that may leverage other softwareapplications. Thusly, the presently disclosed system may also aid theuser in optimizing other programs that are not entirely managed via thissystem. In other words, the user may be able to access other programsthrough interactions between the presently disclosed system and anotherprogram. In this example, the user may wish to execute a marketingprogram. A link to marketing program software may be inserted into aninterface for a software, application, or web page. Although themarketing plan in this example is not executed by the present system,capabilities to execute marketing plans can be incorporated into thepresent system.

The methodology and components described herein may be used for broaderapplications that combine search functionality, whitelist creation, andcustom measurements and alerts to form a custom search monitor beyondsocial media. Custom search monitors can be applied to help usersresearch any topic and compare custom results to another dataset(similar to comparing a target crowd to an existing social community)where item records are capable of being queried in a database. Further,a custom search monitor may operate with direct database access or itmay leverage a web crawler to extract data and populate an index, whichis then searchable by the user. In another embodiment, a separatedatabase is created where new data is added by executing frequent APIcalls of another database or by manual input.

In an alternative embodiment, a custom search monitor may be applied toa store inventory database to alert a user when a new product he wantsis in stock at his local retailer or when the product goes on sale, addsa new feature/color, etc. The product types and fluctuation criteria maybe specified by the user after a search of all records and then trackedto capture fluctuations in specific attributes of interest. Users mayeven use this method to compare one retailer or distributor againstanother along the same dimensions. That is, the user may have an opensearch monitor running for the products he is interested in spanningmultiple stores' databases. A custom search monitor may also be appliedin the context of product user reviews as well. In that way, a user maybe alerted when the products or services he is interested in cross acertain threshold in terms of the volume or sentiment of ratings, oroverall rating score.

In an alternative embodiment, a custom search monitor may be applied toa database of job listings where the user can search and tag targetcompanies he or she is interested in applying to for a job at thepresent time or at some time in the future. The system may then alertthe user to changes to that company's job listings, when new roles areposted, when specific skills and competencies are mentioned in the jobrequirements, or according to any other fluctuation criteria. The systemmay also provide new metrics to the user; for example, the system maycalculate the frequency and percentage of hires in certain functionalareas versus others, and may even provide benchmarking criteria onsimilar candidates as the user. Another embodiment may compare these joblistings to the data contained in a digital version of the user's resumeand alert the user when keywords or other criteria match new listings.That is, a job opportunity may not solely be surfaced based on matchingcompany or job title; it could be surfaced through the presence ofcertain keywords or any other criteria located in the job description orcompany profile. In another embodiment, fluctuation criteria may includeonline mentions of the prospective company or specific online activityof its employees and stakeholders that match the parameters provided bythe user.

In an alternative embodiment, a custom search monitor may be applied inthe context of financial securities and investment decision-making. Thesystem may allow the user to search a database containing various dataon available debt, equity, or derivative securities that meet certainparameters. The system may then allow the user to create a whitelist ofthe securities that he is interested in tracking in either theshort-term or long-term. Fluctuation criteria may vary widely dependingon the user's investment and trading strategy. Examples of fluctuationcriteria include but are not intended to be limited to monitoringsecurities for changes in interest rates, dividend yield, risk metrics,stock price, strike price, share price volatility, moving averages,weighted averages, 52-week averages, price to earnings (P/E) ratio,debt/equity ratio, revenue figures, reported net income, profitabilityindicators such as ROA, recent news announcements and public postingscontaining specific information, etc. Such metrics and measurements maybe leveraged in conjunction with other technical analysis.Advantageously, the system may alert the user when certain fluctuationcriteria may signal a favorable investment condition. The user couldthen use this intelligence provided by the system to optimize his or hertrading and investment strategies, and inform the timing and executionof new trades or other subsequent actions.

In an alternative embodiment, a custom search monitor may be applied toa setting in which a user wants to improve the frequency, timing andrelevance of communications to important contacts. By accessing adatabase of information from different media accounts, such as socialmedia friends/followers/connections, discussion forums, target authorcrowds, a mobile phonebook, email and text messaging accounts, etc., theuser could search for and tag contact records he wants to prioritize fornetworking, relationship-building purposes, or other outreach purposes.Search parameters may include things such as company affiliation,university alumni, professional groups, geographic region, age, keywordor topic mention, interest, image or video share, etc. These and othercustom fluctuation criteria when met may trigger an alert to the user.The system may also deliver an alert after a pre-defined amount of timehas elapsed or when a certain number of contacts in a specific groupinghave messaged him. In another embodiment, the system may display anactivity summary for a certain time period, which would give the user adigest about past conversations with this target contact list andrelevant outreach and success metrics. Furthermore, this group ofcontacts can be tracked independently of all outbound communicationsmade by the user via the accounts he has connected to the system.Thusly, the system may inform the user when he is not regularlycommunicating with the people who he deemed are important in proportionto his total outbound communications over a certain time period. Inanother embodiment, the system may create the feeling of a challenge forthe user to accomplish pre-defined short and long-term goals and provideincentives for doing so. In yet another embodiment, the system may helpinspire the type, substance, and timing of the next message sent by theuser to one or more contacts based on historical context and the uniqueactivation criteria for that contact grouping. For example, say the userhad sent a text message to a specific crowd of target contacts on Day 0about fantasy football prospects. This text message “campaign” had a100% response rate, effectively initiating a dialogue with all of histargets. Further, these target contacts were in turn 50× more likely tostart a new conversation with the user, absent any prompt, within thenext 14 days. This was a significant improvement from his pastwork-related conversation attempts. As a result, the user may decide tospecify fantasy football as new fluctuation criteria. That is, the nexttime this particular crowd becomes activated on the topic of fantasyfootball, the user may then be alerted to initiate a subsequentdialogue. Context-specific relationship-building may be nurtured in asystematic fashion thusly.

In an alternative embodiment, a custom search monitor may be applied toservice offerings as well. In a travel use case, a user may search adatabase of hotels rooms or flights to locate and track preferredseating arrangements, room accommodations, and travel dates to aspecific destination as part of his travel plan. The system may thenalert the user when there are fluctuations in variables that mayexplicitly influence his plans e.g. the ticket price drops or new seatswith extra leg room become available, or perhaps there is a new amenityoffered to him as a traveler. In the event that the user has alreadymade the booking, the system may compare the data on his booked trip toother options occurring at the same time and notify him of anypotentially preferable alternatives that may influence a booking change.

In an alternative embodiment, a custom search monitor may also apply tousers making doctor appointments. The system may allow a user to searcha database of doctors by specifying parameters, such as area ofspecialty, geographic region of practice, # of years in practice,required co-pay, accepted insurance plans, having a degree from aparticular school, etc., and then save groups of their preferred doctorsfor treatment. The system may then alert users when this grouping meetsdesired fluctuation criteria, such as appointment openings with doctors,when preferred doctors are added to the user's insurance provider'scoverage, or even when these doctors open up another practice in theuser's area. A custom search monitor may also be applied in the contextof patient reviews of doctors, facilities, treatments, procedures, andprescription drug usage. In that way, a user may be alerted when thespecific services he may be considering cross a certain threshold interms of the volume or sentiment of ratings, or overall rating score.This information may be useful to different users for benchmarking,evaluation, and research purposes.

In an alternative embodiment, a custom search monitor may be used totrack the publishing behavior of target groups of webpages and thenalert users when these entities cross a certain threshold in contentproduction or search ranking, viewership rate, preference or relevancemetrics, etc. That is, the user may assess competing websites based oncontent goals, content topics, quantity and frequency of content types(blog, whitepaper, ebook, video, podcast, etc,) keyword use, or alongany other attribute or action of interest. The system may also beapplied to alert users when webpages located by a search query adjust incontent or ranking. The system may use a crawler to create a searchableindex of data retrieved from specific website URIs. The system may thencategorize content type based on factors such as URI architecture, whichit may then use to identify and track when new content in those definedcategories are published and then compare it to the entire set of webproperties. The system may also leverage direct database access forapplications with social media accounts: By creating two crowds eachcontaining one social account and then comparing the two (e.g.@Microsoft™ vs. @Samsung™) the system may use the data to extract theInternet media types and content asset types that were posted and sharedby the account most often and track these into perpetuity; this may be anumber of jpg files uploaded, a number of gifs, blog mentions in URIs,promoted content, or any other media type or content asset type.

In an alternative embodiment, the system and methods disclosed hereinmay also be applied to monitoring media libraries that streamentertainment content. Some of the media libraries are available onlineor through a set top box and television. In this embodiment, the medialibrary may be monitored for specific content such as movies ortelevision shows. If a certain television show becomes available, forexample, the user may be alerted to its availability. In anotherembodiment, the user may be alerted when the status of a certain mediachanges. For example, the user may be alerted when a television show istransitioned from being available for a fee to being available for free.Further, the systems and methods disclosed herein may also be used totrack available content from various media libraries generally. Forexample, the system may be able to inform a user which media library hasthe most new material, the most recently added show or movie, or whichmedia library is the largest. If a user is looking for a particular itemof media, the user may be able to use the system to search for thatmedia to determine its availability and the conditions of thatavailability. The user may also determine parameters for a type, length,or genre of media they are looking for. Searching for types, lengths,genres, etc. of media may yield a resulting group of media items. Theuser may also be able to configure and set custom alerts for that mediaor group of media. In other illustrative embodiments, the system andmethods disclosed herein may also be applied to websites that list realestate sales, rentals, leases, etc. Another illustrative embodiment maybe configured to monitor and set up alerts for available parking spotsin an area.

Determining Agility Ratings and Recommended Aspects for Future Content,Actions or Behaviors

Disclosed herein are systems and methods for determining agility ratingsand recommending aspects for future content to be posted by an author,actions to be taken, or behaviors to engage in. As disclosed herein, anauthor generally refers to a person using a social network, website,application software, or mobile application software (app), etc. Whetherthe author actually “authors” posts is irrelevant to theircategorization as an author. A user is described herein as one who usesthe systems and methods disclosed herein. A user may be an author, andvice versa, but a user is not necessarily an author. For example, a userof the systems and methods disclosed herein may merely track thebehavior of authors utilizing the systems and methods without actuallybeing an author themselves. Similarly, authors may exist that do notutilize the systems and methods disclosed herein, but such authors maypost content, generate data, or take other actions that may be trackedand analyzed by the systems and methods disclosed herein. The system canrecommend aspects for future content, actions, and/or behaviors. Forexample, actions and behaviors, as well as aspects of those aspects andbehaviors may be recommended. For example, actions and/or behaviors thatmay be recommended may include examples such as liking a page, creatinga page, editing a webpage, adjusting a page URL or page title, addingalternate text to images, starting an advertisement campaign, conductingA/B testing of a webpage or other marketing message, starting aremarketing campaign, sending an email, purchase decisions, or whether aparticular ad service will address a problem or goal of a user.

A user may wish to determine certain aspects of future content thatwould make the content more beneficial to post or more attractive to aparticular audience. For example, a user may wish to post content onbehalf of an author that is more likely to be engaged with by the userand/or author's target audience. The audience may be a custom authorcrowd as discussed above in the present disclosure. Accordingly, a usermay designate a unique author and a target custom author crowd forfuture content, action, and/or behavior recommendations. The system canthen provide potential requests that each include an indication or typeof a recommended aspect for a future content, actions, and/or behaviors.For example, a system may provide to a user potential requests with aspecific recommended aspect type such as: “How many times should I postcontent today?” “How long should my next content post be?” “What shouldthe subject matter of my next content post be?” “Should I include agraphic in my next post?” “Who and what should I retweet or share?” Inother examples, the recommended aspect types provided with the potentialrequests may be more general. For example, the potential requests may begrouped by type (e.g, color related, length related, type of contentrelated, etc.), goals, difficulty-level, industry or company type, jobtitle or role within a company, persona, use case, etc. Upon providingsuch potential requests to the user, the user can select one or more ofthe potential requests. In one example a potential request is selectedwith a single user interface interaction, such as a single mouse clickor a single touch on a touch screen. In response to the singleinteraction, the system then automatically determines a recommendedaspect of the future content, action, and/or behavior for the selectionof the potential request. For example, if the selected potential requestwas “How many times should I post content today?”, the system mayprovide a numerical recommended aspect response instruction to the userto post content as the unique author, for example, four (4) times today.The system's determination of the recommended aspect can be based onactivity data that indicates aspects of other content authored by orinteracted with by other authors. For example, the system may useactivity data to determine that four is an optimal number of times topost content in a day in order to receive the most interactions with thecontent without alienating authors or causing fatigue with posts by theunique author. Activity data does not necessarily refer to old data.Rather, activity data used for determining recommended aspects isreferred to herein as any data that exists prior to the exact moment intime when a recommended aspect is actually determined. Accordingly,activity data may include data about topics currently trending on socialnetworks, including news from news sources, television, radio, websites,etc. Accordingly, activity data may be updated regularly to provide upto date and real time data for determining recommended aspects. Forexample, the system may determine that a particular hashtag is currentlytrending on Twitter™, and can make a recommendation accordingly. Inanother example, author content may be used to infer potential futureevents, even though activity data is used. For example, the system mayidentify certain authors as having spending power based on live tweetsor other social media posts such as “I want to buy X,” or “Headed outshopping.”

After providing a user with a recommended aspect for posts for a uniqueauthor, the system can also determine whether a post with therecommended aspect is actually posted. The system can then use thisinformation to determine an agility rating that indicates aresponsiveness of the unique author to the recommended aspect. In otherwords, the system generates an agility rating that indicates how good aunique author account is at posting content according to the system'sdetermined recommendation aspects for that account. An agility rating isthe measure of a user's responsiveness to new marketing opportunities.When customer moods shift, or your rival's campaign misses a beat, auser can find out from an agility rating. For example, if the systemrecommends posting four times in one day, the system can measure at theend of the day, or in certain time increments during the day, how manytimes content was actually posted by the unique author the recommendedaspect was generated for. If the unique author has posted four times, afavorable or positive agility rating is determined. If the unique authoralready has an agility rating stored by the system, the four postings inone day as recommended can increase the existing agility rating. Incontrast, if the unique author has posted more or less times than four,the system may determine an unfavorable or negative agility rating (ordecrease an existing stored agility rating for the unique author). Inanother embodiment, a single agility rating may be related to multipleunique author accounts. Such a functionality is valuable, for example,if a user is in charge of multiple unique author accounts. In such anexample, all of those unique author accounts can be used to generate asingle agility rating for the user. In another example, a user may wishto track an agility rating of several competitors or other authors.Accordingly, the user may designate a group of authors for which thesystem can determine a single agility rating. In this way, groupings ofauthors (e.g., by industry, region, etc.) can be used to determine anaverage agility rating of those groupings of authors.

Such systems and methods as disclosed herein are advantageous becausesocial media can be a very variable landscape. Because of its changingcomplexion, marketers need to learn how to strike when it is mostfavorable. Dynamic and adaptive social media campaigns are nowachievable through the systems and methods disclosed herein that canprovide instant answers and recommendations for posting content andquantify a user's ability to leverage these instant answers.Additionally, these methods and systems are valuable because the instantanswers and other functionalities can be customized to fit the user'sneeds. Accordingly, users of the systems and methods herein can adapt tochanging marketplace conditions and produce more frequent, morerelevant, and highly measurable marketing programs that will allowbusinesses to successfully take advantage of the most advantageousmarketing conditions online. Such systems and methods allow sensitivityto current market conditions that are needed to provide meaningfulinformation, and timely and consistent business advice. These systemscan help marketers understand exactly what to do next, and what tacticsto adjust for optimal results based upon what is going on in that user'spromotional climate. That is, the system provides business responses tomarketplace change. These responses can be determined and delivered tothe user in a personalized or generalized fashion. Advantageously, theseresponses are not necessarily limited to the marketing function of abusiness. In turn, users of the system will be able to achieve anincredibly fast time-to-value or time to insight. In other words, a usercan achieve high results without dedicating inordinate amounts of timeto online marketing activities, saving time and money. The system canalso provide individuals who are new to marketing, or who are uneducatedin a particular marketing domain such as social media marketing onTwitter™, tools to learn and implement best practices quickly.

FIG. 7 is a flow diagram illustrating a method 700 of determining arecommended aspect for future content, action, and/or behavior based onhistorical response data of a plurality of authors in accordance with anillustrative embodiment. In alternative embodiments, fewer, additional,and/or different operations may be performed. Also, the use of a flowdiagram is not meant to be limiting with respect to the order ofoperations performed.

In an operation 705, selections of potential requests from a user forrecommended aspect types for future content, action, and/or behavior arereceived. For example, the user can designate which types of aspects heor she may want to request. For example, requests such as what colorshould be used in a post or how many images to include in a post may bedesignated by the user. In an alternative embodiment, the potentialrequests may be predetermined or default such that the operation 705 maybe omitted and the user does not select the potential requests.

In an operation 710, a plurality of authors who are the target of thefuture content, action, and/or behavior are received. The system mayreceive a custom author crowd as disclosed herein or plurality ofauthors who are the target of the future content, action, and/orbehavior. In other words, a user may specify particular authors orgroups of authors to assemble activity data on. This activity data, asdiscussed below, can be used to determine recommended aspects that willoptimize author response and/or interaction. For example a custom authorcrowd may be determined by a search criteria such as a demographic traitor user profile trait of the plurality of authors; a subject matter of asocial media post authored by the plurality of authors; a relatedsubject matter of a predetermined number of social media posts authoredby the plurality of authors; a group association of the plurality ofauthors such as the following of the unique author or the following ofanother author; an interaction by the plurality of authors with theunique author, an engagement with a content by the plurality of authors;an amount of time spent viewing a webpage or screen by the plurality ofauthors; accessing a webpage or screen by the plurality of authors; theselection of a universal resource identifier (URI) by the plurality ofauthors; an affirmative or negative activity executed through the atleast one social network, website, application software, or mobileapplication software (app); or any other type of search criteria. Thecustom author crowd may also be the author's immediate following on asocial networking site such as Twitter™. In another embodiment, thecustom author crowd may be a user's customers. For example, the user canimport or upload a list of Twitter™ handles from a customer databasemaintained by the user. In another example, the user may import ane-mail or mailing list, or any other information that identifies auser's customers. Such identifying information can be used to define acustomer author crowd. In such an embodiment, the system may utilizenon-social networking site specific data (e.g., name, address, e-mailaddress) to search social networking sites and identify authors on thesocial networking sites that likely correspond to the user's customers.This operation may be performed through various hashing methods of datawherein matches are discovered between the social networking site's userbase and the user's customer list without either party having todirectly expose sensitive customer information to the other party.Accordingly, those customers located on the social networking sites canbe included in the customer author crowd.

In an operation 715, potential requests for a recommended aspect typefor the future content, action, and/or behavior are provided. Therecommended aspect type may indicate what kind of recommendation theuser is seeking. For example, if a potential request is “What timeshould I post today?”, that potential request indicates a recommendedaspect type: timing. Numerous recommended aspect types are contemplated,and each of the potential requests or groups of potential requestsdisclosed herein are for at least one recommended aspect type. Thepotential requests may be displayed on a single display such that theuser can see all of the potential requests available for selectionbefore selecting one. The available potential requests here are onesthat were selected by the user in the operation 705. However, in someembodiments, the displayed available potential requests may bepredetermined or default such that they are not selected by the user. Inanother embodiment, the displayed potential requests may include someuser selected potential requests and some predetermined or defaultrequests. For example, if the user only selects one potential requestthey would like to see displayed, the system may display additionalpotential requests in order to still display at least a predeterminednumber of potential requests on the display. The system can bestructured in a personalized question and answer format allowing a userto select their top marketing questions from a list of available options(e.g., check all that apply), and then click one button (a single userinteraction or input) to receive fresh and instant answers each time auser needs them. These requests can also be grouped into categoriesaccording to the purpose, use case, or practical area they serve, suchas “Timing” for informing when certain types of content should bepublished, or “Images” for informing how a user can better utilize imagemedia in his content postings. Other categories may also be used, suchas “Hashtags”, “Strategy, “Community Management”, “Customer Service”,“Website Conversions”, “SEO” etc. Such recommendations can be organizedin a guide or “wizard” view that walks a user through, step by step, howto create, optimize, publish, and/or distribute certain content orengage in certain behaviors, actions, or marketing activities. In thisway, the system can feature additional views and interfaces or displaysupplemental information to provide better instructions and moreintuitive experiences to users. Such resources can provide a templateand/or tutorial experience for using and executing the recommendedactivities described herein. For example, the system may organizepotential requests and their associated recommended aspect types in aquestion and answer format on a display. Other views are contemplated,and may include but are not intended to be limited to a list view, asearch view, and a guide view. With the search view, a user canexplicitly search for a given potential request or recommended aspecttype offered by the system. Search may be done by keyword, for example.The system may also suggest new recommendations for the user based upontheir goals, performance, and recent system usage. The guide view mayshow a more tutorial-based display to provide sequential steps to auser. Such a display can help a user learn how to do modern marketingwith templates and the various systems and methods disclosed herein.Answers or recommended aspects may be unlocked at different stages inlearning process. As the user grows in familiarity and skill (skill maybe indicated, for example, by agility rating) with the system, new setsof potential requests and/or recommended aspect types may appear. Such aprocess can refine a user's content marketing development andpublication or distribution strategies. For example, the system caninclude modules for learning how to use images in Twitter™ contentmarketing, how to do video marketing, how to create original contentbased upon audience trends, etc.

The system can also ask users supplemental questions based on theselected potential requests. The answers can be used as a factor fordetermining recommended aspects of future content, actions, and/orbehaviors. For example, if the user wants to know “Which Twitter AdsProduct Is Best For Me?” the system can return a simple answer such as:Promoted Tweets. However, in an alternative embodiment, the system mayalso ask a follow up question such as: “Do you do email marketing?” Theuser's response to such a supplemental question can inform the systemthat there is some value to this user in capturing new email addressesfor some other programs the user is running. In such a case, the systemcan make different or additional recommendations (e.g. a Lead GenerationCard) as a primary or secondary ads consideration for that user. Theanswers provided by users to supplemental questions may also be used toconfigure and display for the user additional recommended aspects offuture content, actions, or behavior. In the case of the example above,the system may introduce the user to a new set of potential requests orrecommended aspects that are related to email marketing if the userresponded in the affirmative to a supplemental question such as: “Do youdo email marketing?”

In an operation 720, a selection of one of the potential requests forthe recommended aspect type for the future content, action, and/orbehavior is received. In one embodiment, the selection of the potentialrequest of the recommended aspect type is a single input into a userinterface of a computing device. For example, several electronic buttonsmay appear on the screen of a computing device. Each of the electronicbuttons can be associated with one of the potential request forrecommended aspect types for a future content, action, and/or behavior.By selecting one of the electronic buttons with the single input throughthe user interface, the system can respond by automatically determiningthe recommended aspect for the future content, action, and/or behavior.The single input may be various types of input, such as a mouse click, atouch to the location where a button or link appears on a touch screen,a voice command indicating the potential selection, a keyboard or otherbutton selection, or any other type of single input that may beincorporated into the user interface of a computing device. In analternative embodiment, inputs may include voice inputs, such that auser may simply ask his or her device (e.g., smartphone) a question thatcorresponds to a potential request, and the system can determine therecommended aspect based on the verbal question asked by the user. Inother embodiments, more than a single input may be received before therecommended aspect is determined. For example, multiple inputs may beentered by a user to designate additional criteria for the potentialrequest. For example, a crowd of authors the user would like to focusthe request on may be designated, a custom potential request may bedesignated, a unique author that the recommended aspect is to bedetermined for may be designated, etc. In one illustrative embodiment,the user may also specify certain goals, such as wanting to increase afollowing, drive traffic to a website, get a certain number ofretweets/shares, drive new product sales, app downloads, etc. Such goalscan then be utilized by the system when determining recommended aspectsfor future content, actions, and/or behaviors as discussed below. In analternative embodiment, the system may automatically select a potentialrequest to determine a recommended aspect for a future content, action,and/or behavior. In other words, a system may monitor or otherwise checkfor marketing opportunities, chances to post about a trending topic,etc. Such monitoring can determine answers to potential requests (i.e.,a recommended aspect) without an explicit input or selection from auser. For example, a user may make an adjustment or optimization to hisor her marketing mix modeling software, business intelligence software,enterprise resource planning, or other business and operations softwarethat may re-appropriate funds, refine goals, or adjust emphasis oncertain business activities, and business channels, during some periodof time. In such cases, the system may use these events or other inputsmade by one or more users or affiliates of the user to external businesssoftware applications as signals for determining recommended aspectsand/or the degree to which a type, volume, location or context forcertain activity data may now be more or less relevant or significant tothe user.

In an operation 725, the recommended aspect for the future content,action, and/or behavior is determined based on activity data of theplurality of authors who are a target of the future content, action,and/or behavior. The recommended aspect is determined at least in partbased on activity data that indicates aspects of other content authoredby or interacted with by a plurality of authors in a social network,website, application software, mobile application software (app), etc.prior to receipt of the selection of a potential request. In otherwords, the system uses activity data based on posts by authors and/oractivity data about how authors interact with posts in a time before therequest for a recommended aspect is received. In some embodiments, theactivity data may be related to a predetermined amount of time precedingthe selection of the potential request. For example, the activity datamay represent only the last day, week, month, year, two years, etc. ofauthor interaction and/or posts. In another embodiment, the activitydata may include data since the unique author account was formed. Inanother example, the time baseline for the activity data may be relatedto the time since an author account other than the unique author hasposted content or behaved in a certain way. For example, if the uniqueaccount is new and does not have much activity data associated with it,the user may select a different author on which to collect activity dataand use to determine recommended aspects for posts. For example, theuser may select a competitor author account, or an author account theuser or system views as effective that could be used to determine therecommended aspects. In another example, the unique account may havelittle or no activity data associated with it and no other accountspecified to base activity data off of. In this example, the system mayautomatically determine authors to use the activity data from. Theautomatic determination can, for example, attempt to identify an authoror authors the system deems similar to the unique author. Those similarauthor or authors, as well as their followers, can be analyzed todetermine the activity data used. That is, in cases when there is noavailable data for a user the system would provide a generalizedrecommendation for what has worked well for other users of the system orusers which may share common or similar characteristics with the user.In another example the system may use random authors for the activitydata which would generate general recommended aspects as opposed toaudience or crowd specific recommended aspects. In another embodiment,the system may generate a general recommended aspect and an audience orcrowd specific recommended aspect so that a user may compare howrecommended aspects differ based on the activity data used and theauthors associated with that activity data. The activity data may beother content authored by the unique author, content authored by theuser with different author accounts, content authored by related otherauthors such as competitors for a similar good or service, contentauthored by authors who are targeting the same audience and customers asthe user, an internal competitor's authored content (internalcompetitors may be, for example, marketing professionals or users thatwork for the same entity and using separate accounts can fosterperformance tracking and internal competition), any unrelated authors,the author accounts owned or operated by the user's clients, partners oraffiliates, or some combination of any of the preceding options. Therecommended aspects are determined to optimize a response of theplurality of authors to the future content, action, and/or behavior. Inother words, an aspect is recommended based on activity data such that aresponse or other interaction with a subsequently posted content ismaximized for the goals of the user. The response to the posted contentcan be optimized for different factors. Such factors may be determinedby the user prior to submitting the potential request, after submittingthe potential request, or the factors may be predetermined by the systemor default. For example, some factors may be optimizing for responses orinteractions by new authors (authors that have never interacted with theunique author before), optimizing for greatest total number of responsesor interactions with the posted future content, optimizing for aparticular type of response or interaction with the posted futurecontent (e.g., likes, comments, shares, new unique author page likes orfollows, shares with a comment appended), or other types of response orinteraction. The activity data can be analyzed in different ways tooptimize response to posts according to a recommended aspect. Forexample, the activity data may indicate a fluctuation as disclosedherein that is related to the other content that meets a predeterminedthreshold. That is, activity data may be analyzed to determine shifts inhow authors are behaving. Such shifts may be monitored to see if theyreach a particular magnitude or predetermined threshold to determinewhether to recommend a particular aspect based on that shift. Forexample, if a plurality of authors has a subset of authors larger thanthe predetermined threshold that starts posting content related to aparticular live television program, the system may use that informationto recommend an aspect relating to the live television program.Furthermore, a fluctuation criteria may also be used to analyze thisactivity data. That is, the system (as default programmed or selected bya user) may designate a particular type of fluctuation based on thefluctuation criteria to monitor. For example, a bottler of soda in aparticular region may wish to monitor posts of those in his or hergeographical region for social media posts regarding soda, sportsdrinks, sparking water, etc. and the shifts or fluctuations that aretaking place in such an author crowd. Accordingly, the user maydesignate both a custom author crowd for monitoring for activity data aswell as fluctuation criteria for monitoring for activity data. Inanother embodiment, a system may recognize automatically an author crowdand/or a fluctuation that may be meaningful to an author. For example, afluctuation may be meaningful to the user if it is based on a historicalcontext, system usage and user performance, other current activitieshappening on the system or are being currently performed by the user, orfeedback of other users of the system. The fluctuation could also beconsidered meaningful based on past searches, tracked fluctuations,criteria for custom author crowds, and/or user goals. In this way, asystem may determine fluctuations that are relevant to a user withoutthe user explicitly setting a tolerance threshold for receiving helpfulor meaningful information about fluctuations in authors or authorcontent. For example, relevance of fluctuations may be determined basedon a determined type or size of fluctuations. That is, the system may,for example, inform or alert a user their crowd or target audience isusing more positive sentiment even if the user did not input positivesentiment as a desired fluctuation criteria or specify the threshold forwhat constitutes a meaningful spike in positive sentiment. A similaranalysis may be used on other fluctuations such as behavioral changes,topics, discussions and comments by users, or other actions orbehaviors. Such information may also be used as disclosed herein togenerate recommended aspects for future content, actions, and/orbehaviors as well as generating, posting, and/or taking action on suchfuture content, actions, and/or behaviors.

In alternative embodiments, activity data used to determine arecommended aspect for a future content, action, and/or behavior may beactivity data related to only one of the unique author's followers. Theactivity data may also be a unique author's friends; likes; individualsone or more levels removed from the unique author's followers, friends,likes, etc.; or other groups of authors related to a particular authoraccount (even an author that is not the unique author). Activity datafor determining the recommended aspects and for determining whetherposted content meets the recommended aspects may be culled from internetplatforms in various ways. For example, data from web pages may beretrieved with a web crawler or API, or any other data scraping or datatransfer techniques. The systems and methods disclosed herein may beaccessible via software-as-a-service or as an application such as amobile app. Information that may be collected to be used as activitydata may include, but are not limited to, various types of data such asauthor timelines, home timelines, recent posts, followers, friendships,follower ids, friends ids, friends lists, followers lists, suggestedusers, list members, list subscribers, list owners, retweets, statuses,comments, shares, updates, photos, videos, animated GIFs, pages, placesand locations, tags, URLs, hashtags, contact information, events,locations other geographic information, device information and othermetadata, trends and trending topics, collections, projects, streams,uploaded media, direct messages, user objects, user account settings,relevant tweets or users matching a specific query, user profile andbanner information, friend graphs, follower graphs, transaction historyand recent purchase activity, other website, marketing and advertisinginformation, mobile information, pins, related pins, most clicked pins,most repinned pins, relevant pins from a domain that match a specificquery, recent snaps, and other important objects and content that may berelevant to a user of the system. The data the system obtains through anAPI can be stored in a database, such as a Mongo™ database. Varioustypes of other databases may also be used, such as a distributeddatabase like Hadoop™. The system can run algorithms on selected slicesof the data either automatically or when requested by the user tocalculate certain recommended aspects. That is, the system is capable ofpre-computing recommended aspects to questions even before a userexplicitly requests those recommended aspects. These recommended aspectscan then pass through the back-end web application and are rendered onthe front-end interface for viewing by users—and can also be accessiblevia a browser extension as discussed in greater length below. In anotherembodiment, recommended aspects and other functionalities are madeavailable through a mobile app. In some embodiments, multiple algorithmsmay be combined to deliver a single recommendation. Sometimes,recommended aspects based on the algorithms can fluctuate. Accordingly,the system can synthesize results from multiple analyses performed atdifferent times with the same or similar algorithms into onerecommendation that can be more reliable and can help users maximizetheir marketing effectiveness or impact.

Various types of recommended aspects are contemplated herein for thefuture content, actions, or behaviors. For example, a recommended aspectcould be a social network, website, application software, or mobileapplication software (app) in which the future content is recommended tobe authored; a time of day in which the future content is recommended tobe authored; a length that the future content is recommended to be; asubject matter that the future content is recommended to be related to;a type of content that the future content is recommended to be; a tagthat the future content is recommended to include; an indication thatthe future content is recommended to be lifestyle content or productcontent; an indication whether to use paid advertising services on asocial network, website, application software, or mobile applicationsoftware (app) in which the future content is recommended to beauthored; a public figure author to engage with the future content; or auniversal resource indicator (URI) that the future content isrecommended to include. Other recommended aspects of future content mayinclude any other additional aspects or recommendations, for example,media to attach to or include with the future content; style, tone, orword choice of the future content; an explanation or justification ofwhy a certain action should be taken; a volume of future content; anamount of time that should be spent creating future content; an amountof time that should be spent distributing and promoting future content;other purchase decisions or investments that should be made by the user;etc. Other recommended aspects and/or potential requests that may beselected may include (but are not limited to): How many times should Ipost today? How many of my posts should contain images? How can I bemore relevant to my audience today? What are some trending topics Ishould know about? How can I insert my brand or voice into an existingconversation that is relevant to my target audience and/or my brand?What is the promotional climate for a paid vs. organic campaign rightnow? How favorable are the marketing conditions? What's the best mix ofpaid and organic content to publish today (may be expressed, for exampleas a percentage or proportion: 1 out of 8 posts should be paid, etc.)?What type of content would resonate most with my audience today (thatis, what type of content should I post)? (Different types of content mayinclude types such as base line tweets, hashtag content, behind thescenes/exclusives, quotes/inspirational messages, blog shares,list-based content (“listicals”), how-to guides, podcasts, ebooks,retweets with comments, media mentions, research & case studies,animated GIFs, webinars, original media, infographics, memes, selfies,PowerPoint™/slideware, photo collage, illustrations, cartoons,animations, branded product content, YouTube™ and Vimeo™ videos, orshorter looping videos such as Vines™ or Instagram™ Video. Types ofcontents that posts can be about may apply to company content (that is,the specific pieces of content that are produced or curated by thecompany) or user-generated content (that is, content that may besolicited from members of the user's audience via promotions,contests/sweepstakes, etc.)) What should I write a blog about today(such a recommendation may apply to a platform, website, app, socialnetwork etc. outside of where the recommendation is derived from (e.g.,use Twitter™ data to determine what to write blog post about onTumblr™)? What percentage of my content should be lifestyle vs. productoriented (e.g., Red Bull™ may promote images of red bull cans (product)or may promote images of extreme sports/adventure (lifestyle; relevantactivities/behaviors/questions of their target audience)? Which Twitter™Ads (or ad products for other platforms) products should I use (e.g,Promoted Accounts, Promoted Trends, Promoted Tweets, App Cards, WebsiteCards, Lead Generation Cards, etc.)? Where should I post more today?Which of your boards/pages/collections/accounts/handles should you focuson more today (can be valuable cross-platform insights based upon theuser's performance: should a user spend more time on Tumblr™, Twitter™,Facebook™, Pinterest™, Instagram™, etc. than they do; or should moretime be spent on a particular account on a particular platform)? Onwhich platforms should I increase my media investment and paidadvertising and is my current budget sufficient? Proportionally, whichsocial accounts should I post the most text, images, video, audio, etc.to today? When should I publish my fresh marketing content today (timingcan be optimized for the type of content in a post)? When should Ipublish image-rich content today? Or user-generated content, like memes?When should I publish video content today? What tweet length is idealfor getting my audience's attention today? What content should Iconsider repurposing for other media? Who just gained more influencethat I should be talking to? Who in my audience likely has disposableincome to spend? Who is most likely to sway multiple interest groups?Who are some of the most impressionable members of my audience? Whatcolor choices will help maximize engagement/response rate? What colorsshould I use in the background and foreground of the images I am postingtoday? What color variations of my products should I promote today? Whatsettings and environments are most appealing to my audience? Should Iuse images of people in my product content? If so, how many modelsshould I include in these images? What hair color of models will drivemore clicks and other desired behaviors (should the person in your imagehave gray hair or a specific shade of brown hair? What eye color? Howcan I improve the reach of my tweets? How can I grow my following? Howcan I improve sentiment about my brand? How can I become moreinfluential? Where will location targeting have the greatest impact onmy online audience? Who am I not talking to that I should be (e.g.superfans/VIPs/top advocates of a brand, people who have complained,people posting about similar topics but are not competitors)? Should Ifocus ads on mobile or desktop and laptop users? If mobile, should Itarget Android or iOS devices? How are my competitors capturing people'sattention? What types of marketing images/graphics and videos aregetting the most traction in my audience? What is the current mood andenergy level of my audience (mood can indicate receptiveness to certaintypes of content)? Should I incorporate more indoor or outdoor settingsinto my lifestyle content? What emotions is my audience expressing rightnow? Which other brands/musicians/celebrities/sports teams/movies/TVshows/etc. is my audience interested in? What photo filters are mostcommonly used by members of my audience? Which members of my audiencewill likely drive the highest engagement rate? How long should mypromotional videos be? What hair colors will drive the most clicks andengagements today? How many different products should I include in oneimage? How many text characters should I embed in my images? Who is themost influential person in my audience? Who is the most influentialperson at my company? Who at my company should I ask to share mycontent? Who is the most influential person working for my competitor?Who could cause the most harm to me if his/her preferences shifted? Whoshould I send my marketing messages to first? How many hashtags should Iinclude in one tweet or content posting? How long should my hashtags be(e.g., less than 15 characters)? What specific hashtags should Iconsider integrating into my posts today? What other words and phrasesshould I use? What is the demographic and psychographic composition ofmy audience? Which of my website pages should I promote/link people totoday via social media? What products on my website should I promote onPinterest™ today? Twitter™? Other social networks? What keywords ortopics should I include in my profile description? How long should myprofile description be? What emoticons should I use in my posts? Whereis my audience most active today? Should I do influencer marketingtoday? To whom? What should I say? Should I do advocate marketing today?To whom? What should I say? Where else can I go for help (e.g., providelinks to outside services)? How can I improve my top-of-mind awareness?How can I get a certain person or author to join my community/attracthim? Should I explicitly ask people to retweet or share my posts? ShouldI run a contest or sweepstakes today? If so, how and where should Ipromote it? What images should I share today? What videos should I sharetoday? Whose content should I re-pin, re-post or retweet? Who should Ithank on social media? What should I do to change a particular author'sperception of me? How can I improve the global perception of my brand?In a particular geographic region? What product category should Ipromote today? If I am a mass-market retailer, should I post shirts,sneakers, furniture, appliances, etc.? What are people who are pinningmy pins doing/liking right now? What size photos should I use? What'sthe character “archetype” of my target audience or ideal prospect? Wheredo they shop? Where do they eat? What entertainment do they enjoy? Howfar apart should I space out content I publish during the day (e.g. mytweets? How long should I wait in between sending each post? If I'mrunning a campaign, when should I promote it throughout the day withdifferent posts? How should I space out my posts that are specificallyfor a hashtag campaign? When should I follow people during the day? Whatday of the week? How much of my budget should I reserve for “floating”campaigns that are opportunity-driven? Which locations should Igeotarget? Should I target a new whitelist of authors, or a specificwhitelist I'm not using right now? Where do I get one? What characterlength is best for one hashtag? What hashtags is my competitor usingthat I should know about? What angle should I position my photo to lookthe best on each marketing platform? Should it be a landscape or aportrait style image? How closely zoomed into the product should we be?Should I include a hashtag within my image? If so, how many? What typesof charts and graphics should I use to visualize my data? Bar charts?Pie charts? What type of infographic should I create? How many differentcolors should I include in my infographic? How many statistics, facts,or data points should I include in one graphic? How much text should Iuse within a marketing image? How many objects should I include in oneimage? Where should the main text or data be placed within the image?What's the best ratio of negative space to have in a marketing image?Where in the image should my largest or main object be? What size textshould I use in my content? What size text should I use in my imagecontent? What font should I use in my content? What font should I use inmy image content? Are there any special characters I should include inmy content? How should I comment on a blog share to get the mostexposure? Who should I follow back? Who should I thank for following me?Who should I thank for retweeting or sharing my post? What should I sayto someone who favorites my content? Should I thank people over privatemessages or is public acknowledgement better? Should I favorite orretweet someone's content if I want them to follow me or engage with mycontent in return? How often should I do that? Whose posts should Iretweet? Does this change every day? Multiple times per day? How manytimes should I retweet the same person? When should I @mention people inmy content? Who should I @mention in my content? Should I get in thehabit of answering questions on certain topics? How often should Ianswer customers' questions sent via social media channels? What, ifany, questions should I pose to engage my audience today? How oftenshould I favorite or retweet posts made by my followers? How many timesin a row should I post my own content without interacting with membersof my audience? How can I news-jack popular discussions going on rightnow? What news content should I share with my followers or my audience?What product category should I emphasize in my marketing promotionstoday? Should I share stories with my audience that I discovered viaGoogle Alerts? When? What should the subject of my next video be? What'sthe best balance of user-generated vs. branded content to promote orsolicit via a marketing platform, like Twitter? What size/dimensionsshould my images be? What aperture setting should I use on my camera?Should I use flash when taking certain images, like product photos? Whatlight exposure should I use? What focal length is best? What type ormodel of camera should I use? Which photo filters should I use to engagemy audience? What other edits or enhancements should I consider makingto my photos? Should I post the exact same content across multiplesocial networks or accounts I manage? How much time should I invest oneach social network today? If I want to launch a particular campaign,which social networks should I use to promote this campaign? Whichcampaign idea of this set will have a higher success rate with my targetaudience? How do I know when to stop a content campaign? What are thesigns I should look for? What's the ideal follower/following ratio forme to have? Is there a point when it hurts me if I am following manymore people than follow me? When follower growth stalls, or I losefollowers, how can I jumpstart it again? Should I post the same tweettwice in one day? The same image? If so, how long should I wait untilposting it again? What should I post to get account X to favorite it?Retweet or share it? If I mis-spell or make an error in my contentshould I attempt to delete the original post and then re-post my contentor just post my content twice? What is the maximum time period I shouldgo without posting new or original content? What is the maximum timeperiod I should go without interacting with members of my audience? Howmuch lifestyle content should I post in one day? In what order orsequence should I post lifestyle and product content during the day? Howdo I get my first followers? What should my first post be? What words ortopics should I include in my profile description? What cover graphic,banner or primary imagery should I feature on my profile? Which hashtagsshould I include in my profile description? How often should I update myprofile page? Do I need to? How do I find a list of some people I shouldfollow or interact with on a social network? How many people should Ifollow per day? How frequently should I retweet in one day? Whataudience segments is my competitor most interested in? When do I run therisk of being “muted” by my audience or followers? How do I know if I'vebeen muted? How long should my tweets be if I attach media to them, suchas images? Does my target audience exist on this social network? If so,how large is it? What should I not post about on a social network, e.g.on Twitter? Are there certain topics I should avoid or save for othermarketing channels? Are there certain words or phrases that my audiencehas an aversion to? Account X represents an ideal customer of mine, howdo I get more people like him/her to follow me, interact with mycontent, or purchase my products? Who is my top advocate? Who are themost influential people in my audience? Who are the most influentialpeople working at my company? Who at my company should I ask to share mycontent? Who are the most influential people working for my competitor?Who could be potential ally or marketing partner of mine? What landingpages should I link to in the content I publish today? Why are mycompetitors doing better than I am? How can I improve? How can I createcontent that is most amenable to media placement? Graphics? How do Iknow if a news story is really ‘news’ within my audience? Will I be theone to break it, or am I late to the game? Should I increase (ordecrease) the price of my product or service? Should I emphasizeproducts in price range X or price range Y today? Will my audiencelikely be compelled by more discounted offerings today or premiumofferings?

In various embodiments, recommended aspects may be constrained ordetermined based on factors other than activity data. For example, otherfactors may include a user or unique author's advertising budget, apredetermined number of target future contents, available time to spendon marketing activities, a size or location of a target market, theavailability of other tools and resources, etc. For example, if a userhas a limited budget, the system may restrain from recommending numerouspaid content posts. If an author has a stated goal to post content fourtimes a day, the system will not recommend posting more or significantlymore than four times. This may be useful if, for example, a user haslimited man power or time to post content. Recommendations may also bebased on a size of a target market or any other factors of a targetmarket. For example, a user may produce large wind turbines. In thisbusiness, the user may sell a significant amount of its product to avery small number of consumers. Accordingly, page likes from non-windturbine consumers would not be very valuable to the user. Instead theuser may wish to focus posted content efforts on a particular subset ofauthors that is more likely to actually purchase wind turbines.Accordingly, the recommendations may be constrained by the size of thetarget market and the types of engagements or interactions soughtthrough the posted content.

In an operation 730, a content and/or a content template is generatedaccording to the recommended aspect. In other words, when a recommendedaspect is determined, the system may use that aspect to generate acontent for posting or a content template for posting that is generatedaccording to the recommended aspect. For example, if the systemrecommends posting about a baseball pitcher that is eight innings into aperfect game that is being broadcast on television, the system maygenerate a post for the user to post, such as “WOW! Sandy Koufax is ontonight! #lightsout #perfectgamebid.” In other words, the system canprovide automatic content generation. The system may do so based onvarious factors or inputs, such as the posts of other authors about thesame content. For example, the hashtag #lightsout may have been used byauthors the last time a pitcher was having a great game. In anotherexample, the hashtag #perfectgamebid may be in use in posts during thecurrent game for which the automatically generated content is about.Other ways to automatically generate content may also be used.

For example, a user may use the system to monitor discussion ofprofessional athletes, such as NBA stars and their teams. The system maymonitor fluctuations such as when discussion peaks during game (e.g.,when a player hits a game-winner), which can trigger automaticadvertisement generation and/or posting for apparel and other brandedpromotional products that are relevant to that player and franchise.This may happen on a social network where the discussion is taking placeor through other marketing channels such as websites, email, text, etc.In another example, sports apparel brands sponsor many professionalathletes in order to build relationships with the fan bases of thoseathletes. When a star athlete has a strong performance during a game,discussion of that player may spike on social media. When this happens,consumers may have been activated on the topic of the sport, and thegame, the teams, and that star athlete (among other things). Fans mayeven decide to follow a certain account or engage with certain socialmedia posts during or after the game. Activity fluctuations such asthese represent prime opportunities to promote branded apparel productsand services to those audiences. Accordingly, the systems and methodsdisclosed herein can be utilized to both automatically generate contentwhen such an opportunity arises and automatically post the content,recommend or execute a campaign, etc.

In an operation 735, the content with the recommended aspect is posted.Here, the user may actually post the recommended content in the uniqueauthor's account. Where an action or behavior is subject of arecommended aspect, the system can facilitate performing the action orbehavior or scheduling the action or behavior. In some embodiments, theunique author may not be an account controlled by the user. For example,the user may be tracking a competitor's author account. In this case,the content would not be posted to the competitor author account.

In an operation 740, the impact of the posted content is determined. Theimpact is determined by how the plurality of authors responded to orinteracted with the content posted according to the recommended aspect.In this way, a user or unique author can determine whether a recommendedaspect and subsequent content post was effective. For example, thesystem may determine how many likes, comments, shares, retweets,mentions, etc. a content post has received. The system may display thisimpact to the user or author, and may also send an alert to the user orauthor to let them know how effective their action was or group of theirrecent actions were. In another example, the system may measure theimpact of the content more indirectly. For example, instead ofdetermining authors direct interactions with the content posted, thesystem may evaluate other subsequent behaviors, activities, orcommunications made by the plurality of authors on one or more socialnetwork to determine the effectiveness of the posted content. In anembodiment where a recommended aspect is related to an action orbehavior, the impact of the action or behavior is also determined. Thesystem or user may also evaluate the impact of the content, behaviors,or actions through certain business software and performance measurementdashboards that capture, aggregate, and display data from other areas ofa user's business such as sales numbers and website performance.

In an operation 745, an agility rating is determined or calculated basedon the unique author's posted content as it compares to the recommendedaspect. The agility rating may be determined based on the discussionbelow with respect to FIG. 8.

In an alternative embodiment, the system may use activity data topredict the impact of a recommended aspect of a future content to beposted, action to be taken, or behavior to engage in. In other words,the system can, based on all the retrieved activity data, determine theexpected outcome of the future content if it is posted, action if it istaken, or behavior if it is engaged in according to the recommendedaspect. For example, the system may determine a recommendation that auser should post about a presidential debate thirty (30) minutes afterthe debate begins. Based on activity data of author actions during livetelevision events, the system may determine thirty minutes into theprogram is the best time to post. For example, the activity data mayindicate that an estimated 20% more total authors will view or interactwith a content posted about 15-45 minutes after the beginning of anationally televised live event as a opposed to the rest of the debate.Accordingly, the system has output the recommended aspect of posting 30minutes into the debate. Accordingly, the user may also be informed thatfollowing this recommended aspect when posting results in 20% moreinteractions with the post. This can help a user engage with the systemand motivates the user to use the system and actually post content basedon the recommended aspect because they get an indication of what thepost will do before it even happens. The system may further estimate anabsolute impact of the posted content if it is posted according to therecommended aspect. For example, as above, the system may havedetermined that 20% more users interact with a content that is postedabout 30 minutes into a live television event. To determine a baseline,different factors may be used. For example, the type of televisionevent, the unique author's current follower base, time of day, and otherfactors may impact what a baseline of estimated interaction would be. Asan example, the system may determine that a baseline estimate number ofauthors who will likely interact with a post from the unique authorduring the presidential debate would be about 15,000. Accordingly, thisbaseline number can be displayed to the user, alongside an indicationthat, if the recommended aspect is followed, the system estimates anincrease of 20%, or 3,000 increased interactions up to 18,000 if theuser posts content at the right time during the debate. Again, thisestimate and display may encourage further use of and engagement withthe system.

FIG. 8 is a flow diagram illustrating a method 800 of determining anagility rating indicating a responsiveness of an author to recommendedaspects for future content, actions, and/or behaviors in accordance withan illustrative embodiment. In alternative embodiments, fewer,additional, and/or different operations may be performed. Also, the useof a flow diagram is not meant to be limiting with respect to the orderof operations performed.

In an operation 805, a recommended aspect for future content, action,and/or behavior based on activity data of a plurality of authors isdetermined. The recommended aspect may be determined similar to therecommended aspects discussed above at length with respect to FIG. 7. Inan operation 810, the recommended aspect is provided to the uniqueauthor. In another example, the recommended aspect may be provided tothe user. In yet another example, the unique author and the user may bethe same person or entity, so the recommended aspect may be provided tothe unique author and the user. Similarly, if a unique author hasmultiple users associated with the unique author, the recommendationsmay be sent to the multiple users associated with unique author.

In an operation 815, the system determines whether the unique authorposted the future content or with the recommended aspect, action, and/orbehavior. In other words, the system determines whether the user orunique author has actually taken the recommended aspect and postedcontent or engaged in an action or behavior accordingly. For example, ifthe system recommended a time of day to post content, did the user orauthor post content on or around that time of day?

In an operation 820, the system determines an agility rating thatindicates responsiveness of the unique author to the recommended aspect.The agility rating measures a user or unique author's aptitude atresponding to and posting content that is in accordance with recommendedaspects. In order to determine the agility rating, the system may firstdetermine if content with the recommended aspect has actually beenposted by the user or the unique author that is being agility rated. Theagility rating is ultimately designed to measure how well a user orauthor reacts to recommendations provided to it by the system or inother ways (i.e., the agility rating may still be utilized to measureeffectiveness even if another platform is used to generate recommendedaspects for content, behaviors, etc.). In an alternative embodiment, auser may publish content, run an ad, execute an e-mail campaign, or takeany other action or behavior that may not be detectable by the system.For example, this may occur when the content, action, or behavior is outof the platform of the user's author accounts. Accordingly, the inputsused to calculate an agility rating may be received in different ways.For example, the system may allow a user to manually input data thatindicates a response, success, feedback, etc. of the user's out ofplatform content, action, or behavior. For example, if the user runs ane-mail campaign that is sent to the user's 2,000 Twitter™ followers, thesystem may prompt the user to manually enter how many of those 2,000followers followed a link in the e-mail to the user's webpage. Such ametric can be used to determine agility ratings even if the responseitself cannot be detected by the system. In another embodiment, a manualinput of a response may be input by a user with voice recognition: theuser may tell the system what the response was like. Such ratings can bederived for a user's product or accounts or can be derived for acompetitor author account that is not controlled by the user. In thisway, an agility rating can also indicate how well a competitor isreacting to opportunities in the market. The agility rating may furtherbe determined based on whether the user or author has posted a pluralityof future contents, taken a plurality of actions, and/or engaged in aplurality of behaviors that have or are associated with a plurality ofrecommended aspects. In other words, the agility rating may reflect auser or author's aptitude over time at responding to various recommendedaspects and actually posting content with those recommended aspects.When determining the agility rating, it may be based on a plurality ofrecommendations and posted contents over a predetermined amount of time.For example, the predetermined amount of time may be related to a periodof time that occurred most recently relative to the determining of theagility rating. For example, the agility rating may capturerecommendations and subsequent posts within the last three (3) months.In another embodiment, the agility rating may calculated for differentdiscrete periods of time. For example, a user may calculate two of theauthor's agility ratings for the first six months of last year tocompare how the author is doing in the first six months of this year. Inanother embodiment, the agility rating may relate to an entire amount oftime than a user or author account has been active, which would yield asort of all-time score for the user or author.

Other various factors may also be used to determine an agility rating.For example, the agility rating may be a relative score. For example,the system may also track how internal (other professionals within thesame company or organization) or external (outside) competitors areresponding or reacting to recommended aspects for content posts (whetheror not those competitors are actually receiving or being notified of therecommended aspects). The system can use this information to determinewho is reacting to the recommended aspects better or worse as comparedto each other. Thus, a relative agility rating can be determined, ratherthan just a concrete agility rating that goes up or down based merely onwhether a recommendation is being followed. In this way, an indicationof how well a user or author is doing as compared to his competitors,peers, or other users attempting to reach the same or similar targetaudiences can be determined. Another way an agility rating may bemodified or customized is by selecting a custom author crowd for theactivity data. In this way, the recommended aspects are determined basedonly on activity data relating to a particular group of authors. In thisembodiment, different users may have specified different crowds orgroups of authors, which can lead to different recommendations. Forexample, User A may be advised to post eight times a day, while User Bmay be advised to post twice a day. If User A misses one post (for sevenout of eight), they may receive a relatively higher score than if User Bmisses one post (for one out of two) because User B missed a much higherpercentage of their opportunities than User A. That is, an agilityrating determination may be adjusted based on the recommendations that auser or users have received. In other embodiments, the agility ratingmay be determined based on a number of recommendations the user accessesper time period, the number of webpages or screens the user visits, thenumber of goals the user has set, the number of goals the user hasactually attained in a certain time period (or all time) using thesystem, whether or not he is outperforming industry or pre-establishedbenchmarks, etc. The system could also take into account whether theuser followed recommendations for what are deemed to be more advancedtactical recommendations by the system that may require additional time,effort, experience or know-how, and budget to execute on. In anotherembodiment, the system may take into account effectiveness of futurecontent posts, actions taken, and/or behaviors engaged in to determineagility ratings. For example, if a post is effective at generatingpositive interaction, that may positively impact the agility rating. Inother words, if a fluctuation of content generated by a plurality ofauthors is significant or meets a particular threshold, it can be usedto determine the agility rating. The fluctuation may be a change inauthored content, author action, or author behavior by the plurality ofauthors occurring over a period of time and with respect to the futurecontent, action, and/or behavior. An agility rating may be a percentileas described herein, a letter grade, a 1-100 rating, or any other typeof scale or rating. The system can also display agility ratings in tiersas compared to other users. This display can be anonymous with un-namedparticipants or could actually show the names of other users. The systemcould also show high performing and/or low performing users.

Some users, for example those who pay more, may also have the abilityfind out more about and have access to more features relating to thecomputation of the agility rating. The system can provide these userswith precise actions and recommendations on how to improve his/heragility rating. Such a functionality may even interrelate a list ofmarketing recommendations that were delivered to the user, and thendisplay how each one had impacted the user's agility rating. Forexample, such a report may show to the user that he/she was very good atfollowing X category of recommendations, but not Y category ofrecommendations. As a result of this performance, the system would thendemonstrate the impact the user's tactics had on his/her agility rating.If a user had neglected to follow all recommendations about the timingof new marketing content, or had followed only 3 out of 10, then it maybe a very simple concept to convey to the user that if he/she posts eachtype of content at the suggested times, then he or she will see anincrease in their agility rating. Similarly, it may be important for theuser to learn precisely which set of recommended tactics he/she hadoverlooked or perhaps misinterpreted or what specifically contributed tothe decline or growth in the user's agility rating.

FIG. 9 is a flow diagram illustrating a method 900 for alerting a userthat the user's agility rating has dropped below that of a second userin accordance with an illustrative embodiment. In alternativeembodiments, fewer, additional, and/or different operations may beperformed. Also, the use of a flow diagram is not meant to be limitingwith respect to the order of operations performed.

In an operation 905, the system monitors agility ratings of a pluralityof users. In an operation 910, the system determines that an agilityrating of a first user has dropped below the agility rating of thesecond user. In an operation 915, the system sends an alert to the firstuser indicating that the second user has passed the first user inagility rating. Multiple other types of alerts and/or differences inagility rating between a first and second user may be determined inother embodiments. In alternative embodiments, the system may alsomonitor for different thresholds or comparisons between the agilityratings of different users or authors. For example, the system may alsodetermine that the first user's rating has risen above the agilityrating of the second user. In another example, a user's agility ratingmay be monitored in comparison to an average of other user's agilityrating, such as a group of competitor author accounts. In otherembodiments, the system may monitor for predetermined metrics as opposedto or in addition to comparisons with other authors. For example, if auser or author reaches an all-time high agility rating, the user may bealerted of that. In another example, if a user or author's agilityrating goes significantly down or up by a particular threshold orpercentage, the user or author may be warned. In another embodiment, thesystem may monitor for any movement up or down (increase or decrease) inthe agility rating. In another embodiment, the system may send thealerts to someone other than the author or user that the alerts areabout. For example, if a user is tracking competitors, the user mayreceive alerts on author accounts that they do not control. In anotherembodiment, a company may have a large staff of marketing professionals.Each marketing professional may have his or her own user account to usethe system disclosed herein on behalf of one or more of the company'sauthor accounts. Supervisors of the company's marketing department cantherefore receive alerts, agility ratings, and additional informationbased on the individual marketing user accounts to monitor performanceof their employees. Further, the marketing professionals may beorganized into teams based on multiple author accounts owned by thecompany. In this way, by receiving alerts, agility ratings, etc. aboutthe multiple author accounts, teams of marketing professionals can alsobe monitored on their performance. Furthermore, a plurality of authorsor users may also be ranked by agility rating in this way. For example,a leaderboard showing a top 10 in an industry or at a company (or teamof a company) may be maintained. In another ranking methodology, thesystem may calculate a percentile rank to indicate how the user orauthor is doing. For example, if a user's agility rating is higher than90% of his peers at a marketing firm, he is ranked at the 90thpercentile. In order to select a group of authors for ranking,comparing, tracking, alerting, etc. a user or author may use a searchcriteria to define a custom author crowd as disclosed herein for suchfunctions. Using a search criteria to specify a custom author crowd canbe effected in different ways. For example a user may interact with auser interface and display to specify a custom author crowd. A user mayspecify authors they would like to include in a crowd or may specifyauthors whose followers should be included in the user's custom authorcrowd. For example, a user may type in a brand name or brand names, auser may specify Twitter™ handles, upload a list of usernames/actualnames/brands/etc., the user may choose predetermined crowds from a dropdown list or checklist, etc. In another embodiment, a potential requestmay be selected to yield a determined recommendation for authors to addto a custom author crowd. Output reports of agility ratings, history,etc. can be downloaded, emailed, etc. to their team of users, a boss,etc. showing how each of the tactics of each user has individuallycontributed to their goal and increased the overall engagement, agilityrating, etc. of the brand. The systems and methods disclosed in thisapplication to determine and provide recommended aspects andpersonalized recommendations may also be applied to a variety ofeducational and higher education contexts and settings to facilitate theteaching, learning, understanding, and application of certain materials.Student performance, teaching methods and standards vary; thus, morecustomized approaches may be beneficial. Variations of the systems andmethods of the presently disclosed system may also be used to helpinstruct, measure, enhance, and benchmark student knowledge in a set ofbusiness-related or non-business related subjects. Further, thegeneration of an agility rating may also be applied to incentivize orotherwise encourage certain behaviors, actions, goals, and outcomes ofparticipants which may include both instructors and students and otherparties directly involved in or influencing the education process.

Alerts may be utilized in other embodiments. For example, a user canspecify a recommendation and specific threshold or customized criteriawhich must first be met before the system sends alerts to the user withnew marketing opportunities, or calculates fresh marketingrecommendations (these may be in-product alerts, via email, text, pushnotification, mobile alerts etc.). The alerts could be based onfluctuations, agility rating, size of custom author crowds, etc. Alertscan also be generally sent whenever new information is available forviewing. Such alerts may also be used to signal as to when the user hasreached a certain recommendation limit or quota with their account—orwhen a user is underutilizing his or her allotted quota, or some otherindicator of system usage and the user's current position relative tosome threshold or benchmark. This may help the user preserve theirbudget or offer opportunities for the system to offer the user paidcontent and/or access to certain features. Some content available topaid users can be the ability to refresh results, recommendations, etc.frequently in an on-demand fashion. Other users, for example, may onlyget to update their results, recommendations, etc. infrequently, such ason a schedule (e.g., once a day, hourly, set number of refreshes in atime period). Another way features may be divided between paid and free(or discounted) users is, for example, a free (or discounted) user mayonly have one author account in which they can determine recommendedaspects for future content posts, actions, and/or behaviors, while apaid account may have 2, 5, 10, unlimited, etc. author accounts based ontheir paid status or may have different number of author accountsdepending on how much is paid. This may be valuable for those managingmultiple brands or wanting to track competitors, or targeting discreteor multiple audiences.

In some embodiments, a target audience or market is not contained, or isonly partially contained, within an author's current following. In suchan embodiment, customer author crowds are useful. For example, a usermay want to know when it is best to post new Vine™ videos to reachAudience A vs. Audience B; similarly, the user may want to know whetherpromoting an infographic or podcast will be more effective at engagingtheir target audience at 3 pm on a Friday, or for meeting some specificmarketing/business goal they have with respect to that target audience.In another example, a user wonders when they should tweet if they wantto engage video gamers vs. fans of Manchester United™. If they work inmarketing at EA Sports™, and are trying to promote the next FIFA™ game,they may want to know about both crowds.

When first accessing the system, users may be able to viewpre-selected/pre-computed recommendation aspects based upon their priorusage of the system. That is, users may not want to go through theexercise of re-checking or re-selecting each desired recommendationaspect every time they use the system. Instead, the system canpre-populate these results for the users to view quickly without extraeffort. In this way, the user will have the option to either select anew set of recommendations in his current session or have a shortcut toacquire fresh insights from the same set of recommendations as his priorsession. In a similar fashion, a user may receive a suggested set ofrecommendations that the system determines may be of interest to theuser. This will serve the dual use case of experimentation andconsistency. In other words, the system can save past potential requestselections as well as the recommended aspects determined in response tothe potential request selections in order to allow for easier use. Ahistory for a user may include information such as the recommendedaspects delivered, when they were delivered to the user, how long therecommended aspects are fresh or the time/date at which they expire andmay no longer be valid, when the recommendation was acted on by theuser, whether or not the user actually followed the advice of thesystem, other information that was input by the user and used by thesystem to provide a certain recommendation, and/or the impact each ofthese marketing tactics had on a particular goal or goals of the user.In another example, a user may receive a suggested set ofrecommendations that the system determines may be of interest to theuser, which the user may or may not have previously accessed. Benchmarksor industry averages may also be shown for comparison. A user can alsosubmit, through the interface, new potential requests that they wantadded to the system.

In an illustrative embodiment, the system may receive requests ofrecommended aspects to monitor over time. For example, a user may inputa potential request such as, “When should I post product related contenttoday?” In the previously described embodiments, selecting this requestmay generate a time, such as 3 PM (post meridian). After receiving thisselection, the system may send an alert to the user at 3 PM or at apredetermined time before 3 PM to remind the user to post contentaccording to the recommended aspect. In another embodiment, the systemmay receive a selection of a custom author crowd and, the system mayalert user if that custom author crowd grows or shrinks based on thecriteria that specifies the custom author crowd. For example, a user mayindicate a criteria that designates a custom author crowd of everyone onFacebook™ who likes a page relating to Fig Newtons™. In one example, thesystem may determine that this custom author crowd grows to reach onemillion authors. In accordance with this determination that the crowdhas reached a predetermined threshold, the system may generate an alertthat includes a recommended aspect for a content post. For example, thealert may recommend that the author post a celebratory message relatingto gaining a one millionth user of fanatical Fig Newtons™ followers. Inthis embodiment, the alert may be determined automatically, or may bedetermined based on preset characteristics determined by the user. Inother words, the user may have preset that they would like to be alertedwhen one million users is reached, or the system may automaticallydetermine or have default criteria for generating alerts in this way.The system may also automatically determine that such events representnew marketing opportunities for a user.

In another illustrative embodiment, the system may determine an agilityrating or a change in agility rating in advance of a user or authoractually posting a future content, taking an action, or engaging in abehavior with the recommended aspect. In this way, a user or author maybe able to know how their agility rating will change if they postcontent according to a recommended aspect. In another example, thesystem may determine a different metric in advance of an actual post offuture content, action taken, or behavior engaged in. For example, thesystem may predict how a draft post should be best optimized to achievea different user goal, such as sales from a website or new registrationsto a mailing list, subscription service, clicks and engagement,impressions/reach, etc. This can help foster further engagement ofauthors and users with the system because they can see the benefits offollowing the recommendations. In another embodiment, the user and/orauthor may also get to see the change in their agility ratingimmediately after posting a content that follows a recommendation. Inother words a user and/or author's agility rating may be updated in realtime to further improve engagement with the system. For example, thesystem may scan the contents of a post currently being drafted as wellas any uploaded media, metadata, the current time, etc. to see if theuser is following the recommended aspects. In another embodiment, thesystem may send an alert or message regarding a change in agility ratingor potential change in agility rating (if the user has not posted thecontent yet) to a competitor in order to spur on the competitor to usethe system as well. In another embodiment, a real time indication of howan agility rating may change can be calculated and displayed while anauthor is in the process of authoring a post. For example, if arecommendation is to make a post between 80-120 characters, an authormay be writing a post and see his or her agility rating go up when thepost goes higher than 80 characters and goes back down if the post goesover 120 characters. In another example, the recommended aspect may beto author a post with three hashtags. While three hashtags will resultin the best increase in agility rating, the system can also showincremental increases in agility rating as one and two hashtags are putinto the post. Once a content is actually posted/published, the agilityrating can go up permanently based on the content. In another example,the user's draft tweet could be 50 characters in length and have aninfographic attached to it with the “#funfacts” hashtag. The system maythen tell the user that, in fact, 60 characters are ideal (so their textlength may be shown in red coloring) and reaffirm that indeed aninfographic is the best content choice to engage their audience (shownin e.g. green text). It may also approve of the “#funfacts” hashtagbecause it meets a suggested character length and hashtag-per-postquantity. However, the timing may not be right so the system may displayin red—“wait to post until 12 pm.” This same technique could be appliedacross any recommendation on any platform. Such a tool is a powerfulon-demand marketing helper to help deal with many questions a marketingprofessional may have in real time. This tool may also be connecteddirectly to the medium on which the user posts the content. For example,the system may facilitate a user working in the compose tweet window ofTwitter™. In another embodiment, the system may calculate in real time aprediction of how a particular action, behavior, or content will impactother business goals. For example, if a business goal, eitherpredetermined by the system or input by the user, is to increase afollowers for a Twitter™ account, the system can also predict/calculatehow a post will increase followers to the account. In anotherembodiment, the system may calculate an impact and/or agility scorechange while actions and/or behaviors are being scheduled. For example,in a social media management system, such as HootSuite™, a user canschedule posts or other actions/behaviors (e.g., selecting day/timecontent should be posted). A user may also specify a location to tag apost (e.g., Boston Mass.), or select different targeting options for apost, action, or behavior (e.g., country, mobile device, specificcrowd). A user may also engage in behaviors like creating certain metatags on their website to describe certain content, page title tags, oruse alternate keywords as part of a strategy. For example, the strategymay be to drive a higher ranking in organic search results. In anotherexample, a user who is using Photoshop™ to edit or createimages/design/graphic for a post the system can determine in real-timethat if the user, for example, cropped an image in a particular way ormade certain filter adjustments to it, the image may be more likely todrive favorable results for a business goal and/or agility rating. Otheractions or behaviors can also be monitored in real time to determine howbusiness goals and/or an agility rating will be impacted. For example,templating decisions for a website or website optimizations may bemonitored. In another example, page speed/page load changes or otherperformance optimizations for web visitors, fixing broken links or deadpages in a website navigation may be actions that are monitored by thesystem in real time to determine how would that action impact businessgoals and/or an agility rating. Another business goal that impact may bedetermined for is search result optimization. For example, if the useris updating a web site, the system may automatically determine howlikely a web search for a particular term will result in finding theuser's website.

The systems and methods disclosed herein may also include a registrationprocess. First a user may login to the system or create an account. Insome embodiments, login names such as a Twitter™ handle may be used tologin to the present system as well. This may be useful if, for example,the system operates utilizing Twitter™. In this way, the system can usethe login information to access data from Twitter™ to retrieve activitydata and post content. Authorization protocols such as OAuth may be usedto obtain tokens from users that allow the system to request data fromnetworks, such as Twitter™'s API. In other embodiments, the system maydirectly access a social network's user database or achieve accessthrough a third party API or data provider such as Datasift™ forFacebook™. Accordingly, the system can collect data like an author'stimeline, profile information, follower graph, friend information,lifetime tweets, last tweet, etc. A user may optionally add inadditional login information or account specifications so that thesystem can retrieve data on multiple accounts.

A user may enter various information to set up an account. Some of theinformation provided may be used to determine recommended aspects forposting content, taking actions, or engaging in behavior. Suchinformation may be changed by the user to adjust how the recommendedaspects are determined in the future. For example, inputs to registerfor an account may include contact information (your name and emailaddress). A further input may be time zone allowing the system toappropriately deliver recommendations related to content publicationtiming. A user may also specify the author accounts (e.g., Twitter™handle(s)) they want recommendations for (i.e. the recommendee). A userdoes not need to be the owner of an author account to receiverecommendations for it. Activity data can be collected from public orprivate information sources on the internet. A user may also specify allor just significant competitors. Such a specification may be byspecifying a brand name, product name, Twitter™ handle, Facebook™ name,etc. In one example, the potential request “How much should I posttoday” may be informed by combining the results of multiple algorithms.One input can be related to competitors' activities. For example, thesystem finds that a competitor named by the user is posting much moreoften than the user, and that competitor is receiving a high postengagement rate, and so the system may recommend that the user shouldincrease the volume of content he/she posts on a given day by somefactor or multiple. Users may also specify one or more goals for theirbusiness or marketing initiatives. These goals may either be long-termor short-term goals, which may impact how the system determines certainrecommended aspects for the user on a given day.

A user may also input products/brands/services, campaign indicators suchas hashtags, etc. that they will be promoting through their user accountor through one or more author accounts. This can be used as an input todetermine recommend aspects for users regarding, for example, thefrequency and proportion of lifestyle vs. product-oriented content theyshould post on a given day. Accordingly, the systems and methodsdisclosed herein recommend that a user balance publishing lifestyle andproduct or explicitly branded content. A loyal following may be bettergained by not talking about yourself and how beneficial your product isall day long. Rather, it can be beneficial to talk about the topics thatare of particular interest to your audience.

A user may also specify the size of their target market. Such data maybe used as an algorithm input for certain recommended aspects or typesof aspects. A user may also specify to what extent they feel theircurrent following contains their ideal targets. This input may be usedto determine an approximate target market. That is, if a user thinksthey have only captured 40% (e.g., 400,000 people) of their targetmarket, their total target market is the 40% (e.g., 400,000 people) ofcaptured authors plus an additional 60% (e.g., 600,000 people) equalingthe total target market 100% (e.g., 1,000,000 people). A user may alsospecify their primary goals, which can be used to determine, forexample, how much and which kinds of advertising the system shouldrecommend that the user do or use. A user could also specify theiradvertising budget, specify the amount of time they spend onlinemarketing, or any other information related to use of or goals in socialmedia. Information such as this can help adjust certain recommendationsaccording to the user's preferences, daily routine, specific businessand team needs, operational/financial feasibility, etc. on certain tasksand recommendations.

After logging in and/or creating an account, the user is shown theavailable potential requests for recommended aspect types and can selectthe ones they want. In some embodiments, certain potential requests maybe available for paying customers. After selecting the potentialrequests desired, the user can make a single click or interaction togenerate all of the recommended aspects. The recommended aspects couldbe displayed on the same page with the potential requests, or could bedisplayed on a separate page or on some other medium leveraged by theuser. In an alternative embodiment, separate account creation may not beneeded by the system. For example, if the system disclosed herein isintegrated into a social network such as Twitter™, a user may log in andaccess the various functions of the system using their Twitter™ handleand password. Advantageously, the presently disclosed system may also beintegrated into different types of marketing and social media managementdashboards, such as HootSuite™, or other business software. Recommendedaspects may be used in conjunction with or to inform tactical decisionsor other tasks that may be executed through or performed while usingother marketing, business, or consumer software applications. Thepairing of the presently disclosed system with other systems may giverise to one or more new or complementary recommendations. Embodiments inwhich the system functionality is interrelated or enhanced by othersystems, or vice versa, are contemplated.

The systems and methods may be executed on computing devices in variousways, such as those discussed above with respect to FIG. 1. Another wayin which the disclosed embodiments may be executed is through a browserextension that will allow users to access their recommended aspectsoutside of a web application. A browser extension can allow users toaccess their personalized insights on the fly. These can be valuable forusers who are busy using other web-based software platforms to createcontent and engage with customers (e.g. HootSuite™ or Sprinklr™),sending emails, or when they are on Twitter.com. That way, system doesnot force users to be on an additional webpage or inside an applicationwhen they want to harvest new insights using the systems and methodsdisclosed herein. Instead, the system can deliver these answers to thespecific context in which they are desired by the user. Through abrowser extension, users are given the opportunity to view updatedanalysis results from the set of recommended aspects just as would beavailable to them in the web application. The users may be able tochoose from a fresh set of questions via the browser extension. In yetanother embodiment, users may be limited to the set of potentialrequests and recommended aspect types, which they had accessed duringtheir last session not using the browser extension or mobile app.

In one embodiment, the system may, without specific selections ofpotential requests from a user, still determine recommended aspects forfuture content, actions, and/or behaviors. For example, the system canmeasure fluctuations in data (user, author, crowd, entire network,etc.). Such fluctuations can be measured manually (directed by a user),but may also be scheduled by a user or automatically scheduled on theback end (a default schedule). For example, data processing may occurfor all users in one-hour increments so the system may provide hourlyanswers. The system can then identify new marketing opportunities basedupon fluctuations in data and/or the user's current climate, position,and goals (i.e. identify favorable marketing conditions for the user).The system can also convert these opportunities into practical insightsand one or more suggested marketing tactics. The system can also learnfrom the user's performance or fulfillment of certain recommendedaspects. Based upon the user's subsequent activities (and successes andfailures) and those of other users' of the system, the system mayprovide additional recommendations or suggested tactics for the user.The system may also adjust a user's recommendations to make theperformance of certain tactics more manageable. Such adjustments may bemade based on time availability, financial resources, access to certaintypes of content and media (for example, if the user has no way ofproducing an infographic, the system should learn from that—either byuser input or via the user's activity—and instead recommend thenext-best type of content the user should post instead), or generalperformance/fulfillment of recommendations. In one example, if thesystem recommends the user should post 10 times each day, but he/sheonly posts 4 times, it may be a function of the available time that userhas in the day to devote to social media marketing. Therefore, thesystem should learn from this user's activity and adjust the futurerecommendations like this that are delivered to the user until he/sheattains more steady fulfillment of each recommendation. That is, theoptimal recommendation may be unrealistic for the user to fulfill rightaway; but he/she may learn to do it over time.

Other features can also be included in various embodiments. For example,users can export recommendation aspect results into a spreadsheet suchas Microsoft Excel™ to save for later viewing and/or analysis. A usermay also be able to download other document types, such as PDF orPowerPoint™ files. The system may provide a “Refer A Friend” link, wherethe user will have the opportunity to choose between sending apre-populated tweet or an email message to a specified recipient andinvite them to use the system. Users may also have the ability to accessadditional content through the system that provide enhanced tutorials,guides, and extra helpful information that will help them execute andimprove upon their marketing. There may also be a leaderboard that isviewable to users containing performance measures of other users of thesystem, or a forum in which users of the system can interact with eachother to share advice and best practices, or ask for help. The systemmay also provide a workspace where the user can experiment with, design,store, or test different variations of his or her content. The systemmay also provide a series of pre-loaded samples of recommended aspectsfor popular authors to illustrate how users of the system, who may workfor companies of different sizes and industries or foreign countries,may be able gain meaningful insights from the system.

The systems and methods disclosed herein can be integrated with anysocial network. Other social networks the system could integrate withmay include but are not limited to: Pinterest™, Yelp™, Flickr™, Meetup™,Tagged™, YouTube™, Vine™, Facebook™, Instagram™, QQ™, WhatsApp™, QZone™,LinkedIn™, Skype™, Google+™, Viber™, Tumblr™, Snapchat™, LINE™, SinaWeibo™, VK™, Reddit™, StumbleUpon™, Foursquare™, etc.

The concept of generating recommended aspects based on potentialrequests can be used in other sectors of the marketing industry. Forexample, recommended aspects can be determined as disclosed herein forareas including but not limited to: display ad spending, mobile adspending, radio ad spending, SMS and text message marketing, SMS andtext message ad spending, search engine marketing, search ad spending,TV ad spending, email marketing, email ad spending, b2b marketing, leadgeneration, content marketing, public relations, market segmentation,mobile content, market research, digital marketing and websitemarketing, display marketing, programmatic advertising, real-timemarketing, marketing automation, loyalty programs, couponing, socialcommerce and social selling, mobile and tablet commerce, e-commerce,omni-channel retailing, cost-per-click, local search, mobile search,paid search, search engine optimization (SEO), mobile social media,native advertising, ratings and reviews, social media marketing, socialmedia ad spending, digital video, digital video ad spending, mobilevideo, mobile video ad spending, video marketing, print mediaadvertising, direct mail, in-store displays, outdoor marketing, eventmarketing, non-traditional and guerilla marketing, telemarketing,cross-media marketing, daily deals, graphic design and illustration,other art forms, etc. Accordingly, the systems and methods disclosedherein are not meant to be limited to a certain medium or type ofmarketing.

The systems and methods disclosed herein can also be used to manage andsystematize marketing experiments. For example, users as disclosedherein can consume their tactical and best practice recommendations fromthe system whenever they want, wherever they want. The system may alsoinclude a campaign tactic or a group of campaign tactics that the usercould select to move forward with. Tactics can be directed towards onemarketing channel or involve conducting activities on multiple marketingchannels. Such a system could provide an integrated or multi-channelprogrammatic ad spending mechanism or some other structure where thesystem may recommend a suite of marketing channels, platforms, ordevices for the user to try out that day and then allow the user toexecute certain promotional tactics and new marketing activities. Suchan embodiment could convert answers, trends, and other data-driveninsights into an array of pre-populated tactics, methods and campaignsthat the user may choose to trigger, test, modify, or ignore. Suchtactics could include a group of recommendations across multiple socialnetworks, for example. Social networks or sellers of advertisingservices may offer users of the system promotional access to advertisingservices to increase their business. Similarly, social networks orsellers advertising services may also offer the systems disclosed hereinas a promotional tool to their existing customers in order to helpincrease per customer ad spend. The systems and methods disclosed hereinmay also convince users who have floating or ongoing marketing campaignsand flexible budgets to allow companies to react to these new,on-the-fly marketing opportunities determined by the system. This couldalso increase overall ad spend.

For example, the system may partner with multiple advertising platforms,such as Google Adwords™, Twitter™ Ads, and Facebook™ Advertising. Thevarious advertising products offered by these platforms can be displayedin the user's campaign menu. The system can automatically create acustomized marketing tactical response or suggested activity set for theuser based upon that user's freshly available recommended aspects forcontent posts and what is going on in his or her audience or theadvertising medium generally that day.

The system may have already learned from user-submitted information thata user's primary marketing goal is to drive installs of a new mobileapplication. The system can determine based on previous results with anadvertising product or a stated purpose of an advertising product whatmight work best to accomplish those goals. For example, an App Cardproduct by Twitter™ Ads may be recommended for this goal. The user mayhave also specified a secondary goal of growing his follower base. TheTwitter™ Ads product that may be recommended for quickly gainingrelevant followers can be a Promoted Account. The goals of a user may becaptured by the system with explicit reference to a single advertisingmedium (e.g., what is your top goal on Twitter™) or they could be theuser's overall goals for social media, marketing, or his business. Theremay even be some required interpretation by the system of whichadvertising product is a preferred solution for meeting a certain goalof the user—if this information is not readily available or open tointerpretation.

However, at certain times online marketing climate conditions can beused to direct a shift in advertising strategy as determined by thesystem disclosed herein. Accordingly, a user may experiment with anadditional or alternative advertising products, mediums, suppliers,services, etc. based on a current climate at a particular time or on aparticular day. Recommendations to experiment in this way can bedetermined through analysis of variables in activity data as disclosedherein. For example, when a user has identified a set of competitors,the competitor accounts may experience a spike in follower growth ratesand have an order of magnitude larger number of followers than the user.In such a case, it may be justified for the user to shift priorities forthe day to use, for example, a Promoted Account product instead of anApp Card because the current climate indicates that there are manyauthors out there following new pages or accounts that are similar tothe user's. Therefore, switching to a strategy designed to get morefollowers would be well timed in this scenario. Additionally, usersdiscussing or engaging regarding similar content may be tightlyconnected and actively engaged in retweeting messages that day, so theremay be opportunity for the user to receive more exposure for the user'sTwitter™ handle as well—an additional incentive to try to reach thosetargets right now. In this case, the system can present Promoted Accounton a screen as a proposed tactic that the user may choose to execute a)along with a promoted App Card, b) ahead of or in place of an App Card,or c) in combination with some other set of suggested marketing tacticsor campaigns. That is, advertising tactics considered to be advantageousmay be calculated for and suggested to a user irrespective of thatuser's explicit goals or established marketing plan. This new suggestedad can include a performance estimate indicating the expected impactthat the ad may have and pre-populated creative elements, such as text,images, designs, tags, and other inputs. Additionally, the template ofthis new ad could be based off an existing message or a previous adcampaign developed by the user.

Suggested targets or goals could also be generated by the system and canbe customized and edited by the user at various points in time. Targetscan include but are not limited to factors such as location, gender,language, devices, platforms/operating systems and carriers, keywords,followers, interests, tailored audiences, TV targeting (shows, networks,genres etc,), other behaviors and characteristics, the placement ofwhere this ad will appear on Twitter™ (e.g. in users' timelines), etc.In one simple example, the system could detect that a certain percentageof the user's target audience is active in New York right now (apotential request regarding geographic locations of active users mayhave been selected or the system may have determined this on its own).The system may then automatically input and recommend “New York” as aspecific criterion for geographic targeting for a suggestedadvertisement on Twitter™ at that time. The user could also add targetsor exclusions to any of the recommended targets; certain exclusions mayalso be suggested by the system. For example, the user may target theentire United States except California for a product advertisement. Theuser may also specify a total budget, daily budget, a custom bid, or maxduration or time frame for a selected campaign.

In another embodiment, the system may suggest and provide the capabilityfor a user to execute a new Facebook™ ad. A suggested Facebook™ ad couldbe shown in the display of a user's computing device as part of amarketing menu alongside the tactical advertising suggestions forTwitter™. The display of all of these suggested items may also reflect aranking or sorting of the recommended tactics by order of importance orin terms of suggested priority to the user, shelf life (how long untilthe opportunity expires, or will need to be recalculated), or by therelevance and predicted impact of each tactic the goals of the user, orby the time and effort it will take for the user to execute therecommended tactic.

In another embodiment, the user's goals may be different for differentmediums. For example, a user's top goal on Facebook™ may be to useadvertising to increase online traffic and conversions on the user'swebsite, while the user's goal on Twitter™ is to increase the number offollowers the user has. The system may, sometime after the goals wereset, detect that the user's target audience for Facebook™ websiteconversions activated in the geographic area of the user's headquartersthat day may be perceptive to liking the company's Facebook™ page. Forexample, a high proportion of these authors' online posts contain wordsand phrases, which are deemed to reflect positive sentiment. Given thisenvironment, the system may recommend that the user leverage asupplementary advertising tactic on Facebook™ that day to capitalize onthis new change. Because of the opportunity to drive local awareness ofhis company, the user can be alerted to and given the opportunity toexecute a Facebook™ ad to promote his business—specifically to peoplewho are nearby that area. This newly surfaced promotional opportunitymay not have been something the user planned for. Instead, theopportunity or potential request was triggered by current conditionsthat may have a small time window of opportunity. Similarly, thissecondary recommended aspect may be shown next to, or proceeding, aprimary recommended aspect for the Facebook™ advertisement that the usermay leverage to attain his goals with respect to new websiteconversions.

As disclosed herein, the system may be integrated with other marketingplatforms outside of social networks. For example, the system may beintegrated with Google Adwords™. In such a case, the system may beintegrated with one or more of Google's APIs or the user's personalGoogle Adwords™ account in order to gain access to important informationlike the user's keyword planner, display planner, current searchmarketing campaigns, the user's organic search rankings, the user'sgoals and preferences, transactions, performance history, current trendson Google™ search volumes and other industry data, the user's availablebudget, and other settings and information. The system may use this datato determine goals for the user and as factors for determiningrecommended aspects of content. For example, the system may detect afluctuation in search volume, bidding, or some other indicator around aparticular keyword that may indicate a new opportunity. Suchopportunities, like those described on Twitter™, may warrant a newmarketing campaign or tactical combination of campaigns to be conductedby the user.

The system may also suggest recommendations of an explicit pairing of apaid search advertisement with other advertising tactics in a user'smenu of possible future content posts, such as a Twitter™ PromotedAccounts campaign. These tactics may use targeting criteria that allowthe user to reach authors with similar characteristics on differentplatforms. However, in some cases it may be preferable to use differenttactics on different platforms to appeal to different audiences. Forexample, the demographics of authors on the social network Pinterest™may be very different from the demographics of authors on the socialnetwork Spotify™. Accordingly, the user may direct different tactics orcampaigns to those respective networks. Each suggested tacticalcombination may contain recommendations for using certain words,phrases, or design elements to help ensure a consistent brand identityor to more effectively appeal to each audience in each setting.Similarly, the content such as postings or advertisements in thissuggested tactical combination may leverage the same features or systemoutputs that were actually derived from activity data or fluctuations inthe user's target audience or social network. For example, the systemmay recommend to the user that they should use a new trending hashtag#fantasyfootball, and participate in that hashtag conversation, tobecome more relevant to the user's target custom author crowd. In oneembodiment, the system may automatically insert, or suggest inserting,#fantasyfootball into the message text for each advertisement that isposted. The system may even recommend variations of these ads, or promptto the user to include other insights from system that the user couldtest by operating multiple campaigns or tactics at one time. Thesefields could be automatically pre-populated by the system or otherwiseinput by the user.

Furthermore, the system can automatically tag each tactic or collectionof tactics in a way that facilitates tracking and measurement of amulti-channel (or platform) opportunity campaign. For example, if auser's product is a mobile app that includes a music streaming service,the system may recommend to the user that there is opportunity to do apaid search ad on Google™ using the keyword “live internet radio” basedupon other activity around that keyword and the recent search habits ofconsumers. Similarly, the system may recommend using a related keywordto the user's current keyword set. The system may also recommend thatthe user take this insight and apply it to another marketing channel—forexample, to publish a promoted tweet on Twitter™ that contains the exactkeyword “live internet radio” in the text of the tweet or some variationof that keyword or concept. Alternatively, the system may recommend anew keyword or set of keywords to promote on Google™ based upon theexpressions, activity, and/or behavioral fluctuations of that user'starget audience on Twitter™ or some other preferred advertisingmedium/platform.

The system can identify new opportunities, generate recommendations totailor one or more suggested tactics to the user, and can present suchrecommendations or tactical sets on a display in a menu format where theuser could decide whether they want to initiate, postpone, ignore,deactivate, or abort a particular tactic or campaign recommendation. Putanother way, the user is given the flexibility to choose from a set ofsystem-generated recommendations or instead do something that is notexplicitly listed. That is, the user may propose an alteration to abaseline tactical suggestion, make an explicit modification to atactical suggestion, or choose a new marketing tactic autonomously. Thesystem can structure experiments to only be run within certaintimeframes, which can provide the user with greater transparency intowhen, where, why, and how the advertising or marketing tactic isapplicable and appropriate for the user's current situation. The usercan therefore a gain deeper understanding of how a campaign fits withinthe bigger picture of his or her collection of online and offlinemarketing programs.

The system can also adapt to current marketing conditions the user isfacing at the present moment, as well as take into account constantfluctuations in data and changes in competitor and other participantactivities. The system can also automatically adjust campaignexperiments and the delivery of new tactical marketing opportunities tousers according to the explicit goals, information, and other criteriasubmitted and set by the user.

The system allows the user to ramp up his or her activity and mediainvestment on the integrated advertising mediums in order to expand thescope of their marketing activities seamlessly. For example, if a userhas available budget, a smaller experiment can be expanded or scaled upinto large-scale campaigns very quickly with the systems and methodsdisclosed herein. The system can achieve this by recommending additionaltactics, duplicate tactics, alternative timing of marketing content,follow-up recommendations, or proposing investment in a new marketingtool or advertising medium that is currently not being used by the user.As previously discussed, some of these experiments may be free trialsoffered by an advertising platform as an incentive to capture new usersand expand the engagement and ad expenditure of existing customers. Assuch, the system provides an incentive or process for the user tosubscribe to a more premium level of access and additional functionalitywith the ad provider. The system may thus become a lead generation toolfor a social network or ad provider.

The combination of intelligently determining recommended aspects forposted (paid and unpaid) content and a dynamic opportunity classifierallows the system to appeal to the needs of a wide range of companiesand provide mechanisms with which the system can onboard, educate, andramp up the online marketing activities of the user. Accordingly, thesystems and methods herein are worthwhile for a small business that haslittle to no knowledge about social media best practices or a largebusiness that wants more advanced monitoring, recommendations, tacticaladvertising campaigns, and timely and practical advice. The system alsoallows the user to manage and customize many of their own settings,which are useful for more precise progress measurement and efficientgoal attainment of the user. All of these systems and methodsincorporate and/or work in conjunction with the determination and usageof recommended aspects for future content to be posted, actions to betaken, and/or behaviors to engage in that are based on activity datathat indicates aspects of other content authored by or interacted with aplurality of other authors. These recommended aspects can be embedded inand/or help inform, alter, revise, compel, or otherwise adjust aparticular tactic or marketing strategy/campaign.

Various algorithms may be used for text, behavior, and other analysis toanalyze data, content, authors, etc. and generate recommended aspects.For example, algorithms utilizing machine learning, network analysis,predictive analytics, descriptive statistics, natural languageprocessing, graph algorithms, sequencing algorithms, numericalalgorithms, optimization algorithms, database algorithms, signalprocessing, deep learning, artificial intelligence, etc. may all be usedin various embodiments disclosed herein. The system can also includecomputer vision specific algorithms that allow the system to dosophisticated image processing. Such techniques are used to examineimage (e.g., stills, videos, GIFs) based content to analyze activitydata and determine recommended aspects for potential requests directlyor tangentially regarding image based content. For example, an imagebased potential request may include a request such as: What colorchoices will help to increase my engagement rate today? The recommendedaspect (or answer to the question) depends on activity data indicatingcurrent color preferences of the authors on which the potential requestis centered as well as images they are engaging with most frequently ormost recently. In one illustrative embodiment, the system can determine,based on the activity data, a recommended aspect that indicates theoptimal color pallet each user should use for their posted image contentthat day, and for example the RGB, CMYK or hexadecimal values of thesecolors. These RGB values, and other information extracted from theimage, may be used by the user to inform new marketing design choices inPowerPoint™, Illustrator™, Photoshop™, Microsoft™ Word, other text, wordprocessing, graphic, image or video editing tools, campaign manager andads management/editing tools etc. for example. There may also be aconversion mechanism to Pantone™ color values to facilitate certainmarketing use cases. Advantageously, the system provides a connectionbetween recommended aspects to target an audience and the tools,platforms, and other software products or services that may be leveragedby the user to create, edit, store, or distribute certain marketingcontent. In some embodiments, certain data analysis (e.g., videocontent) may utilize additional integrated APIs (e.g. a YouTube™ API).For example, YouTube™ videos posted or linked on Twitter™ may not beable to be pulled and analyzed using a Twitter™ API as with text orimages. Instead, the system can layer in another data source tounderstand what is actually in a video and analyze it with variouscomputer vision techniques. Integrating additional APIs may be used anytime data from one provider or social network is distributed on anotherprovider or social network. It may also be beneficial in certain casesto correlate, interrelate, or perform other analysis exercises onmultiple data sets in conjunction to enhance understanding and value forthe user.

Results from Google's custom search API, or Yahoo BOSS Search™, or forexample the Bing™ search API can be used to get more information aboutimages shared by users on a given social network or other marketingchannel. That is, the system can, in addition to or instead of relyingon computer vision techniques to understand the characteristics andattributes of a set of images, Google's API can be used to getadditional information about images. For example, the system may be ableto capture certain metadata about the images from a reverse image searchvia Google's search API. Such a process may make determining recommendedaspects faster and easier, because metadata indicating the color palateof an image or sequence of images (e.g., GIF, video) is generallysmaller in size than an actual image, and no image analysis has to beperformed. In other words, the system can use metadata of historicalimage data to generate the color preferences of authors instead ofanalyzing and using the images themselves to determine the activitydata. This can save on processing speeds and potentially increaseaccuracy of the recommendations and activity data processing dependingon the quality of image metadata.

In such an implementation, the system can integrate with a search engineAPI, such as Google's API, to gain more information about the image,such as the websites where that searched image or set of images iscurrently hosted. For example, if the potential request is to provide arecommended aspect to a user about the usefulness and timing of productphotos (e.g., what percentage of your posts today should contain productimages?), the system can determine what exactly constitutes a “productphoto.” Such a determination may be accomplished by looking at the hostsite of that image. Product photos, for example, would likely come fromretailer websites or corporate sites. In this case, the system could useavailable metadata to identify which images in the set were productphotos without having to do complex object detection. However, wherecontext based recognition is not used or cannot be used, complex objectdetection may still be used. In another embodiment, the system may useboth context based and complex object detection. In another embodiment,the user may upload or otherwise provide a means with which the systemcan access a repository of product or non-product images to informcertain recommendations. In another example, the system can identifywhich photos shared by Twitter™ authors are stock photos. Stockphotography may be leveraged in many different areas of marketing thatare not limited to posting content via social networking sites. Acorresponding potential request may be, “Should I use stock photoimagery? If so, where should I use it, and how often?” The system canthen compile (or have pre-loaded on it) its own catalogue, website list,taxonomy or grouping of websites which are likely to contain or offeraccess to stock photography and then look for matches within thosesources. If an image is hosted on a stock photo website, it is likely tobe a stock photo and can therefore be treated as such by the system.Such a technique can be applied across a range of other content types,such as videos, or any other searchable items where a user may beinterested in understanding the origin site, hosted websites, geographiclocations, and other relevant metadata associated with that particularobject.

Web analytics platforms can also be integrated to inform the systems andmethods disclosed herein. For example, the system can use data obtainedfrom web analytics platforms, such as Google™ Analytics, through productintegration. Such an integration can allow an interrelation between thesystem and the activity and performance of the user's website (traffic,conversions, sales, etc.) to allow the system to use this data to informthe system's recommended aspects, tactics, and best practices for socialmedia marketing, or other marketing and business purposes. For example,a user may wish to receive a recommended aspect telling them whichproducts on his website he should promote today on social media sites.In one illustrative embodiment using Google™ Analytics' API, the APIallows a user to request certain information, or even real time data,for an authenticated user. The API allows users to query for dimensionsand metrics that display active viewers of a page, the most popularcontent and pages on a users' website, etc. Such data can be used toinform recommended aspects of future content, actions, and/or behaviors.

In one embodiment, the system may determine through an API that visitorsto the user's website are currently interested in peacoats. Webpagesfeaturing product images of various peacoat styles may be receiving aproportionally higher share of page views, clicks, sales, or a longeraverage session length. As a result, the system may use this data togenerate a recommended aspect telling the user that they should publishmarketing content about peacoats on platform (e.g., Twitter™, Facebook™,Pinterest™) or even recommend explicitly that the user share those exactsame images of peacoats on various social media at that time. Theviewing behavior of this user's website visitors may be indicative of apreference held by others in the user's target market or digitalaudiences who may not know about the particular product offering on theuser's website(s) yet. Accordingly, such a recommended aspect, if actedupon, could be very helpful in driving additional traffic to the user'swebsite, and potentially more sales of the peacoats. It is advantageousfor the user to capitalize on such current trends by publishing morerelevant content about peacoats via one or more preferred marketingchannels. The same technique may be applied to old products or productpages and marketing offers as well as new ones; the user may be alertedby the system to a positive fluctuation in page views for a productlisted on his website e.g. six months ago. This may, in turn, generate aseparate recommendation that the user could consider posting about thispage content (e.g. product/service) via social media as well.

A specific promotional climate on a particular social network can alsobe used to inform the substance, timing, and design elements ofrecommended aspects and subsequent posted content or activities orbehaviors of the user. For example, the hair color of the models used ina suggested image, or the optimal number of products to feature in oneimage that can help the user to maximize the engagement rate with theirpost on a particular medium or help achieve some other goal specified bythe user. Therefore, activity on the user's website, or set of websites,may inspire both new website optimization tactics as well as tacticsthat are unrelated to the user's website. For example, websitestatistics as market signals and inputs can be used to informrecommended aspects of subsequent marketing activities on social mediathat can be executed by the user, and vice versa.

In another embodiment, a user can upload multiple potential images,designs, videos or other media for a future post, and the system canrecommend one based on activity data in a way that indicates alikelihood that the user's audience will like the image or contribute tomeeting a goal of the user. Similarly, a user may also upload multiplefuture content posts or other text and keyword selections. The systemcan determine the contents' character length, word choice, universalresource indicators (URIs) and other website links, grammar, etc and thesystem can recommend one based on activity data, or other market data,and determine the likelihood the user's audience will like it orcontribute to meeting a goal of the user or increasing his or heragility rating.

Numerous (hundreds or thousands) private and public APIs and userdatabases exist that the systems and methods disclosed herein may usingaccording the various embodiments to provide recommend aspects forfuture content, actions, and/or behaviors to businesses and individuals.Some of these APIs may include but are not limited to: Amazon™ ProductAdvertising, Shopzilla™, Ebay™, weather APIs, event aggregator APIs,news service APIs, etc. New APIs may even be created or offered bycertain companies specifically for integration with the disclosedsystem. Such integrations may use the API as an input (e.g., historicalor real-time streaming data) to the system or may serve as outputdestinations where the recommendations generated by the system may beconsumed by or within other software technologies.

Data used for any of the embodiments disclosed herein, including customsearch criteria, fluctuation criteria, comparison of groups of authors,recommended aspect determination, activity data, agility ratingcalculation, marketing campaign recommendations, etc. can be gatheredfrom any type of various electronic devices, software, and sensors. Forexample, devices where data can be collected may include (although thislist is not meant to be limiting) smart thermostats, automobiles,biometric devices such as a FitBit™, smart product labeling, in-storeshopper tracking and geotagging, wearable electronic devices, bicyclecomputers, etc. where data is transmitted via connections to theInternet in various ways such as WiFi, Bluetooth™, etc. Data capturedthrough such devices can be used as historical or real-time data fordetermining certain tactical marketing campaigns and/or recommendedaspects of content for the user.

For example, in-store tracking may be accomplished through Bluetooth™,video capture and analysis of in-store shoppers, WiFi fingerprintingwhere a system tracks the signal strength of a WiFi signal in a store,data from a MEMS chip in smartphones to track a heatmap of customeractivity, LED lighting in a store where frequency emissions are used todetermine customer location, as well as via loyalty programs that mayleverage NFC-enabled cards to track purchase activities at checkout.Such in store activities may be used to determine marketingopportunities to authors and ways to further engage with them in storeand on social media. Tracking authors using electronic devices may bedone in other settings than a store. For example, authors at a sportsstadium may be tracked to determine opportunities to market to them. Forexample, a fan in close proximity to a merchandise booth may be texted aspecial coupon for use at the merchandise booth. Such a campaign maycome to the user in the form of a recommended aspect of a content postas disclosed herein.

The systems and methods disclosed herein may be utilized for a varietyof different purposes by a variety of different users. For example, aconsumer, instead of a marketer, may want to use systems and methodsherein to learn how to improve his or her popularity or credibility on agiven social network. In another example, a user may want to use answersgenerated by the system to help inform a purchase decision. What productor service should they choose? Which store should they purchase it from?Which location? Should they ask for discounts? Where can they find outif there is a coupon for the product? The systems and methods hereincould also inform lifestyle choices: What clothes or shoes should theywear today? What's the current style or trend? What TV shows should theywatch? What places should they visit? Who should they talk to? Whereshould they go to get advice? When should they do different tasks duringthe day? Numerous applications are contemplated where a user of thesystem may apply audience-driven insights in making decisions in his orher own life. Other data rich scenarios that could utilize the systemsand methods herein for decisionmaking include but are not limited tosales, advertising, enterprise resource planning, accounting andfinance, project management and collaboration, healthcare,manufacturing, public relations, human resources and recruitment,research and development, operations and supply chain management,distribution and logistics, customer service and customer relationshipmanagement, IT and service management, purchasing and procurement,inventory, merchandising, quality assurance, market research, insurance,management consultancy and strategy, consumers, etc.

Various embodiments utilizing the agility rating as disclosed herein arealso contemplated. For example, agility rating weights may be adjustedbased upon criteria such as more consistent performance, fulfillment ofwhat are deemed more advanced recommendations provided by the system,timely fulfillment of recommendations based on how pressing theopportunity is, etc. An agility rating may also give preference tosomeone who consistently capitalized on marketing opportunitiesgenerated by the system, that have a shorter time to execute theopportunity or who actually posted in a shorter amount of time.Adjustments to the agility rating may also be directly linked toreal-time or near real time changes in the results output of the system.That is, if there is a change in the output of the system (e.g., afluctuation detected), then that change can represent an opportunity fora user to adjust his or her strategy. Whether the user adjusts his orher strategy can be a factor in the computation of the agility rating.For example, the system may take into account how many new opportunitiesa user received in a given time window and then the percentage of thoseopportunities that were fulfilled by the user.

The following details how the systems and methods disclosed herein maybe used by an example user. For example, the user is a custom t-shirtmanufacturer. The user wants to know what to do on Twitter™ today topromote their business. Initially, the user can set goals and plangenerally how the day will look with regards to marketing. The user mayuse the system to determine recommended aspects for 1) how many timesthe user should post today (e.g. 10); 2) what time of day should be afocus (i.e. should the user concentrate more posts during a specifictype of day or post their longer posts at a specific time of day?) (e.g.11 AM); 3) how many of the user's tweets should contain image content?Additionally, a user may also discover through the system whatadvertising products and opportunities may be used to achieve particulargoals. This may be accomplished through supplemental questions answeredby the user.

The system can also be used to help the user identify new opportunitiesthat arise spontaneously. For example, the system can help the userdetermine what type of content the user's audience is really interestedin right now or may pique their interest. For example, the user maydiscover that hashtag content is receiving the most engagement. A usermay also use the system to determine what is trending in the user'scustom crowd/target audience. For example, the user may discover that“#FreeBrady” is a trending topic, which presents an opportunity torelate the user's brand and offerings to a trending conversation that ispopular within the user's target audience. In order to react to thisopportunity, the user may utilize previous content posted and/or neworiginal content. For example, if the user tweeted last week that peopleshould share the funniest t-shirt designs they have seen, the user andthe system can learn about clever designs and even get content from thet-shirts designs that people shared that can be incorporated in a post.This type of data can be very relevant to the user's audience (becausethe content originated with the user's audience in the first place) andhelp elicit more user-generated content or any other favorable outcome.The user can also determine what kind of original content they want tocreate. In this case, the user may decide to create a t-shirt design andintegrate #FreeBrady directly into the concept art. To promote thiscontent on Twitter™ the user can compose a series of tweets using thishashtag, and then attach an image of the shirt design to at least onetweet. In another tweet, a link to where an author may purchase thet-shirt may also be included.

Other factors for content generation can also be determined using thesystems and methods disclosed herein. For example, colors of the post,generated graphics, text, and t-shirt itself may be determined using thesystem. For example, the system can determine that the color scheme ofthe New England Patriots football team is inherently associated with the#FreeBrady concept and therefore could/should be included in the contentgeneration. The system can also help determine what other colors theuser's target audience prefers and/or is interested in. Thisrecommendation could help the user to decide what colors to use in otherpromotional materials for the product, such as the background forproduct images shared on Twitter™, or the best color scheme for apromotional infographic. The system may help determine other factors fora content post, such as how many models to use in an image, how many ofthe models should be wearing the shirt, time of day to post the imagecontent, what the optimal tweet character length is for the content,etc. The user may therefore compose a promotional tweet based on therecommended aspects and publish the content.

In another recommended aspect, the system may recommend now publishinglifestyle content after the product (t-shirt) posting. Since the latestshirt was football related and a football season may be approaching, thesystem may recommend posting about things like sports news, the Patriotsfootball team, fantasy football, etc. In addition, since the contextsurrounding the #FreeBrady hashtag has to do with football (as Tom Bradyis the quarterback for the New England Patriots football team) it mayalso be preferable for the user to engage in sports or football-relateddiscussion topics. In this way, the user may build awareness andlikability with their target audiences over time. For other subsequentcontent, the user may use the system to determine a type of video thatresonates with his or her audiences. For example, the user may discoverthrough the recommendations of the system that Vines™ are very popularright now, driving high engagement rates. Accordingly, the user canextend the concept of #FreeBrady into another content format and dotesting to see what drives the most engagement. Accordingly, the usercan decide to make a 6-second vine about the production of his#FreeBrady shirt coming off the press. The system may recommend, forexample, to share the video at 3 PM that day. These various postings canbe developed and scheduled within a service for social media postings,such as HootSuite™, or managed through other marketing softwareplatforms.

The user may then try to leverage the system to discover opportunitiesfor more direct engagement with his or her target audience. For example,the system may analyze authored content to determine who is likely tohave disposable income right now (have users recently tweeted aboutgoing shopping or new things they bought?). The system can identify suchauthors and facilitate sending those users a special offer for goods orservices. In some instances, the user may reach out to these authors onTwitter™ or a different marketing channel. For example, although theauthor tweeted about shopping on Twitter™, the system may recommendsending the promotional offer to the author via e-mail if an e-mailaddress is available. The system may also help identify which members ofa target audience are more impressionable (e.g., which authors are morelikely to share content from a company). The system may recommendtweeting at this authors using their handle and including a specialoffer to alert them to a deal. These authors may subsequently share thetweet and further propagate the marketing of the user's products andbrand.

The user may also use the system to determine who is most likely to swayan interest group (e.g., football fans, fashion crowds, Boston crowds,sports crowds, or news/media crowds, etc.) that is part of a user'starget audience. For example, the system may recommend interacting withan author who is influential with a particular interest group. Forexample, the user could offer an influential author something that mayincentivize them to share the user's branded content with those valuableaudiences he/she has influence in. The system may also help the userdetermine who has gained more influence. For example, the user mayrecognize someone that tweets about the product to their followers andgive them a free t-shirt or other recognition. Such authors may be worthbefriending and fostering a relationship with. The system can help theuser determine how to do this through personal tweets, whether to sendtweets publicly or privately, through other communication channels(e.g., phone, e-mail, paper mail) or social networking platforms, etc.

In this way the t-shirt designer user receives timely answers from thesystem about how to promote his company on Twitter™ today. The user evenreceived an idea for a new product variation, which evolved into its ownmulti-faceted marketing campaign. Accordingly, a user may use the systemand methods disclosed herein to learn, for example, promotional guidesand frameworks to focus on for the day; how to construct originalcontent that day; how to make choices about things like product stylesand other merchandising considerations; how to build stronger ties withthe lifestyle needs of the user's audience; how to conduct effectivecommunity engagement on and offline; how to conduct other importantmarketing tasks and behaviors; and/or how to launch a grassroots paidcampaign on top of his/her other activity.

Automating Content Design Transformations Based on User Preference andActivity Data

The world is becoming more and more visually driven. Images and videocontent are among the most important feature of an advertisement. Suchcontent is often referred to as “creative.” Under-performing ads resultfrom sub-par creative, but marketers may not know when their creative issub-par and even if they do know it's sub-par, they may not know what todo about it. There is no easy way to intelligently tune content, such asan image, before, or while, an advertising campaign is running. In someinstances, marketers may not even be aware of the aspects of theircontent that could be improved. Generally, the best thing marketers cando is to test an alternative image (which takes time and resources todevelop).

Some ad serving platform technologies, such as those used by socialnetworks, also penalize the distribution of an ad if click rate slows.Therefore, operators of social networks typically recommend thatadvertisers “refresh” their content regularly. Currently, this is amanual process for the advertisers, and doesn't happen very oftenbecause the design resources on content have already been spent—andthere aren't suggested optimizations that are personalized to eachbusiness and user. The systems and methods described herein can be usedto provide recommendations to modify images, videos or other contentautomatically, with each modification or adjustment configured toimprove an attribute or performance metric of the content, such as amarketing performance metric.

Selecting the right content (e.g., images), and the right attributes ofcontent, can drive more awareness, customer engagement, conversions, andsales. The systems and methods described below can implement a creation,selection, scoring, evaluation, and transformation mechanism which caneither collectively or in part be used as a “perpetual improvementmachine” for business and marketing content and for advertising creativeand in many other areas of design and creative works.

As described above, content (e.g. for use in a marketing campaign)includes “creative,” which may be marketing content and including animage or video, as well as copy (e.g., text). A creative strategyprovides the guiding principles for copywriters, graphic designers, andart directors who are assigned to develop contents, including designs,photos, and advertisements. Creating content can be a subjectiveexercise and tends to not be very data-driven. Further, any insightsobtained via data-driven means or via consumer research lose valuerapidly and often never make it back to those who are actually designingcontent and planning and executing campaigns through advertisingplatforms (e.g., Google, Facebook, etc.) in a timely manner. Theinsights have a very short shelf life and may be closely related tochanges in audience behavior. “Guess and check” techniques are commonpractice. As a result, a tremendous amount of capital is wasted on “A/Btesting” in advertising.

In many instances, personalized recommendations on how to improvecontent are not available. Automated audience-based evaluation systemsor improvement mechanisms for creative assets used in a marketingcampaign or other types of content publishing campaigns are alsogenerally not available. As a result, it is difficult to knowspecifically how to change and adjust content or campaigns to achieve aparticular business goal. For example, it is not easy for a business oroperator to know how to maximize the impact of digital or traditionalmarketing programs during planning, creative production, and execution,or how to recalibrate such a program to achieve a specific result.

Thus, advertising and other forms of content generation and/or contentpublishing today typically begin with an image, a video, audio, or text(or a combination of these) that becomes an ad, and then finds anaudience. This process can be expensive and error-prone. The systems andmethods described herein can use artificial intelligence and othertechniques to first find an audience, and then evaluate, rank, score,give recommendations, and generate new content items that are optimizedfor the target audience. The audience's actual behavior becomes astandard against which new and proposed content, designs, or creativesare evaluated.

The systems and methods described below can adjust and refine content,such as advertising content, in real-time based on the target audience'sactivities. As a result, content can be continuously or periodicallyupdated based on a target audience and the response data to the contentand advertising campaigns (from that audience and other audiences). Thesystems and methods described below represent new technology thatadvances this concept even further with images, videos, text, audio, andother forms of content. They also have applications in other industries,business functions, and consumer applications.

The solutions of this disclosure also can take attributes from a harvestof content items and analysis of audience data associated with theharvest content items to inform the creation of a target audience. Forexample, the systems and methods of this disclosure may determinepopular keywords relevant to a target audience of consumers of aparticular brand. These high-performing keywords, and related words andphrases, that are important or unique to the audience may then be usedin a subsequent analysis of the more general audience of potentialconsumers of that brand to more accurately find, define and otherwisesegment a better set of the brand's consumers on the basis of theirtextual information and conversations. That is, signals derived fromwithin an audience are used to better understand and define thataudience for the purpose of analysis. Through this process, a user ofthe system is able to more accurately find and classify people thatbelong in that audience or may otherwise be considered members of theaudience's extended network for the purpose of conducting a morecomprehensive analysis of the target audience.

As described below, various computer-implemented techniques, includingartificial intelligence and machine learning algorithms, can changemarketing and advertising content, such as by performing contenttransformations on text entries, and on image and video pixels, based onpredictions determined to improve the impact of that content. Ingeneral, a transformation can be any alteration of any portion of acontent item. For example, a transformation may be an image manipulationor generative visual manipulation for content items that include visualcontent. Various types of content transformations are described indetail below. In some implementations, the technology can be applied toone or more types of content at a time, e.g. to optimize three images ora set of text entries. This can be particularly important in the fieldof marketing optimization and customer experience.

This is in contrast to traditional methods for content production, whichmay rely strictly (or heavily) on A/B testing, or no testing at all. Forexample, in an A/B test, image A can be determined to be generating tenclicks per 1000 impressions, while image B is determined to begenerating 15 clicks per 1000 impressions. Based on thesedeterminations, traditional methods would recommend that the contentpublisher devote more spending to image B. Some content platforms (e.g.,social networking platforms such as Facebook) may perform this type ofspending optimization as an option, either automatically or based on aparticular key performance indicator, such as reducing cost per lead orreducing cost per acquisition.

However, the A/B testing process assumes that the creative which wasused in the content is the best it possibly could be. The traditionaltechniques only work with the best content it has at a given time, andthose contents are typically made with very little data optimizations,or none at all. Therefore, the content used in these traditional systemsis already sub-optimal to begin with because it does not include theoptimal characteristics and attributes that the new and improved systemsand methods described below can recommend in the pre-production orduring the production of the content. In addition, the failed A/B testsperformed using traditional techniques can contribute to even morewasted money and time. Further, A/B tests do not provide clear insightabout why one asset fared better than another in a live field test. Thisinformation gap makes it even harder for marketers, advertisers,designers, campaign planners, and brand managers to consistently improveperformance.

Using the systems and methods described further below, optimizations canbe made to content before it is developed at the idea/concept stage,while it is in-development (e.g., in image or video editing software),and via post-production enhancements to the content by leveraging userpreference data. In some implementations, the user preference data caninclude audience data. So, instead of mounting costs, wasted time andenergy, high uncertainty with A/B testing, and overall sub-optimalcontent, the systems and methods described below can help contentproducers to move towards only having to do “A Testing” and beingconfident that the image, text, video, audio, or other creative assetsthey are publishing are maximizing the likelihood that their messagewill resonate with the target audience and drive the best possiblebusiness outcome or goal. Thus, the systems and methods described belowrepresent a significant improvement in the field of design, contentdevelopment, and business and marketing performance optimization.

FIG. 10 is a block diagram illustrating a system 1000 for transformingone or more content items in accordance with an illustrative embodiment.The system 1000 includes a content evaluation system 1005 incommunication with a plurality of audience computing devices 1010, aplurality of content sources 1015, and a user computing device 1020. Thecontent evaluation system 1005 includes a content item harvesting module1030, a performance metric ranking engine 1035, a matching criterionmanager 1040, a recommendation module 1045, a content transformationmodule 1050, a performance metric predictor 1060, audience performancemeasurement module 1065, a user alert module 1070, a graphical userinterface (GUI) generation module 1075, and a database 1080. In general,the system 1000 can be configured to evaluate one or more content items,determine scoring criteria select an optimal subset of content itemsfrom among a group of candidate content items, and/or apply one or moretransformations to content items in order to improve a performancemetric associated with the content items, as described further below.

In some implementations, the system 1000 can receive original content,such as an original image A, and can then create new optimized versionsof it, such as transformed image 1, transformed image 2, transformedimage 3, etc. which can each be a predicted optimal permutation of theoriginal image A. This functionality can effectively scale up theinventory of creative assets for a content publisher without the needfor additional manual work of a graphic designer, whose designiterations are inherently subjective. Stated another way, the system1000 can receive a first content item, such as a first image, and canproduce, for example, five optimal versions, 500 optimal versions, 5000optimal versions, etc. according to user preference data that has beenanalyzed by the system 1000. The system 1000 may also do so inconjunction with user preferences, which may include, for example,creative restrictions, brand guidelines, or marketing requirements.

In some implementations, the system 1000 can determine that a targetaudience likes content item A more than content item B. As a result, thesystem 1000 can transform content item B to look more like content itemA, or to look more like other content items that are similar to contentitem A. The system 1000 is also sophisticated enough to extract featuresfrom a large corpus of content items, such as images, to figure out theaspects of the content that matter the most to that audience. That is,the system 1000 scales and becomes more intelligent as more contentitems are added to a model implemented by the system, which may includeartificial intelligence and machine learning algorithms.

In one example, before a content publisher conducts a campaign based onat least one content item, the system 1000 can be configured to make thecontent item resonate more with an audience including people who likehiking. The system 1000 can take the original content item and transformit to look, sound, or feel more like the content items that peopleinterested in hiking are engaging with, or have engaged with previouslyon one or more media or websites, etc.

Using the system 1000, real-time adjustments can be made to the contentitems, based on real time feedback. This real-time feedback can be basedon response data to the advertisements themselves (e.g., as measured bythe performance measurement module 1035) or based on activity datawithin the target audience, which may include the audience computingdevices 1010, or a combination of both of these datasets. The system1000 also may use other information to inform the content transformationprocess, including television commercials, weather forecasts, sportsscores, entertainment events such as local concerts, award ceremoniesand festivals, news, holidays, location data, new business, marketing,communications or public relations initiatives, etc. The transformationmade to the content items by the system 1000 can be an automatedimprovement on a candidate content item, or can be an incrementalimprovement to a content item that has already been optimized by thesystem 1000 at least once. Thus, one or more transformations may beapplied at any given time to a particular content item or content items.

In some implementations, the system 1000 can trigger imagetransformations based on new events within a target audience while anadvertising campaign is live in action. When consumers start talkingabout a topic, mention a keyword, express a certain emotion or behave insuch a way that indicates a particular mood state or desire, or share orengage with a certain type of content item (e.g., image or videocontent), it can trigger the system 1000 to perform a new transformationon a content item. For example, if the system 1000 determines that anaudience of sports fans begins talking about the NBA and the ClevelandCavaliers, the system 1000 may adjust a content item used in a campaignto reflect the characteristics of content that is of interest to thataudience. For example, the system 1000 can transform the content item tolook, sound, or feel more like basketball-related content bytransforming the content item to share similar features and attributes,scenes, objects, word choice, etc. of content items determined to be ofinterest to that audience. Alternatively, the system 1000 mayspecifically transform the content to look more like content that anaudience of Cleveland Cavaliers fans is likely to engage with.

In some implementations, the system 1000 can transform a content item ifmembers of the target audience are speaking positively or negatively. Insome implementations, the system 1000 can perform a different type oftransformation when members of the target audience express some otherkind of emotion, such that the content item can evolve over time. Forexample, if one or more members of the target audience are expressingsentiments of anger and frustration, the system 1000 may transform thecontent item to have a look, sound, or feel that is more calming andapproachable. If a user is depressed, for example, the system 1000 maytransform the content to include aspects that would be humorous oruplifting.

In some implementations, an audience may be an individual person orentity. Thus, if an individual expresses a negative opinion regarding auser (e.g., a content publisher), the system 1000 may can avoidpresenting content items that don't have identifying informationregarding the content publisher, such as a logo, inside them, or contentitems that contain a special offer from the content publisher. Inanother example, the system 1000 can determine that an audience memberrecently visited several websites about baby care products. As a result,the system 1000 may transform a content item to be displayed to thataudience member to emphasize themes relating to family, parenting,fatherhood, etc. In some implementations, the system 1000 can derivesuch transformations based on other successful content items that weretargeted towards an audience of new fathers, or based on the recentcontent that was viewed and or engaged with by this particular audiencemember. Thus, the system 1000 can use many types of factors regarding anindividual's behavior or engagement (or engagement of a largeraudience), and can transform the content items delivered to theindividual to look, sound, or feel like the content items the individualengages with most and may have a preference to see and react to or spendtime on. Additionally, the system may transform the content to look,sound, or feel like content that the individual may not have seen orengaged with before, thus providing “freshness” and novelty.

The system may also be used to refresh content items and creatives thathave been previously published or are currently out in the field, e.g.as part of an ongoing digital advertising campaign. In one embodiment,these contents may be fed into the system either via API or via manualuser input, or via a cloud storage or content management system, orother means, with certain performance data and metadata. The system willthen take this information into account when producing a newtransformation for the content item. In that way, it may be referred toas a “second-generation transformation.” For example, if the audiencehas been shown an ad of a red apple four times per day, the system 1000may determine that the audience has been fatigued by red apples due tothe high frequency with which red apples appeared in other content itemsdisplayed to the audience. As a result, the system 1000 may determinethat the next transformation of this content item should de-emphasize oreliminate the prominent red attribute. Thus, the transformation mayproduce, for example, an image of a green apple rather than a red apple.Alternatively, the system 1000 may remove or replace the apple withinthe content item for this audience. The purpose of such a transformationwould be to “refresh” the content item for the users with the intent ofincreasing the performance of the content in the audience.

The system 1000 can perform transformations based on any type ofperformance metric associated with a content item. For example, thesystem 1000 can be configured to make a content item “stand out” fromother content items, which may not necessarily produce an optimalclick-through-rate or marketing outcome. In this example, the system1000 receive information corresponding to various activities of audiencemembers, or in a particular individual's browsing or viewing history,and can transform the content item so it looks unlike other contentitems the target user has seen before. For example, if the individualhas never been shown as having a lime green theme or engaged with a limegreen post on a social networking site, then the system 1000 mayrecommend that transforming a content item by adding a lime-greentreatment to the content item is likely to get the individual'sattention. The system 1000 also can perform a transformation of thecontent item to generate a transformed content item having the limegreen treatment. There are several of these types of transformationsthat may vary based on a content publisher's expressed or inferred goal,or based on the current market and business conditions or consumerstate. A list of example marketing use cases in which the system 1000may provide beneficial content transformations is described brieflybelow. One of skill in the art will understand that the examplesprovided below are intended to be illustrative only, and should not beinterpreted as limiting the scope of this disclosure.

The system 1000 may also recommend that a particular element, feature,style, or category of image is unpopular in an audience and/or unlikelyto lead to the user's intended marketing outcome. For example, if thesystem 1000 determines that a user-provided image contains an image of ababy but the audience has not responded favorably to baby images, or ifthe system 1000 determines that this particular baby image is unlikelyto produce greater brand recall, memorability, or sales performance forthe brand product, the system 1000 may recommend to the user to insteaduse an alternative image, e.g. a family photo. The alternative imagefeature or category can be predicted to perform better in the targetaudience. In another embodiment, the recommendation of an image, photo,design, or creative execution may be informed by previous campaigns andcreative choices of the user or by other external factors that mayimpact the performance of the user's creative executions. Such externalfactors may include the creative executions of competitive brands.

In another embodiment, a user of the system 1000 may define a “creativetarget” based upon a set of images provided to the system 1000 by theuser. In such an embodiment, the user may select or provide images,instructing the system 1000 to transform a candidate content item (e.g.image) to be more like the examples the user has provided. That is, thesystem 1000 can determine optimal characteristics in the provided imageset and apply transformations to the new content items based on what thesystem 1000 has learned about that input set of images. In such anembodiment, performance criteria of images in the target audience may beabsent or ignored, as the user of the system 1000 has articulated thedesired design parameters or other preferences for his/her candidatecontent item by inputting them into the system 1000. In that way, thesystem 1000 can be “overridden” by the preferences of the user for howthe user would like the design creation to appear.

In another embodiment, the user may specify that the candidate contentitem is a Christmas or holiday-related promotion of a product. In suchan embodiment, it may be the user's intention to make the content itemlook more appealing among the target audience than other Christmascontent items in circulation (i.e., content in the same or similarcategory to the user's content). The system can then recommend uniqueenhancements to the content to achieve this result, as it may be a goalto drive more sales relative to competitors. In such an embodiment, thesystem 1000 may also take special consideration, or apply special weightand emphasis, to the characteristics of Christmas-related content itemsin order to make the candidate content item better stand out among allother Christmas-related content items the system 1000 has analyzed. Inthis embodiment, the system 1000 may leverage audience data to achievethis result, or it may look at audience-agnostic signals that arepertinent to the Christmas/holiday category of content.

In another embodiment, the user may upload past images used in ane-commerce store, such as a storefront on Amazon.com or the brand's owne-commerce web domain. In some implementations, the user may provideperformance data with these images (e.g. product photos) indicating therelative success of each image used. Performance metrics may includeelements such as the click-through-rate. In this example, the user maywant the system 1000 to learn from the product photos used on thestorefront in order to recommend a better product photo to use in placeof one or more currently used photos for the purpose of improving thesales performance of the product, or products, listed on the e-commercestore. In another embodiment , labeled datasets on past advertisingperformance using certain image or video contents may be uploaded by theuser.

In another example, the user may provide performance data on an emailcampaign along with the images used in those campaigns. A common emailmarketing performance measure is the click rate or click-through rate.In such an embodiment, the user may desire to learn how to use betterimages in email messages in order to get more clicks from emailsubscribers. The user may also wish to reduce the unsubscribe rate ofthese emails. In such implementations, the system 1000 can use this datain order to recommend image selection and transformation to improve theeffectiveness of subsequent email programs to this subscriber base.

Sample Marketing Use Cases:

-   -   Improve the aesthetics and visual appeal of a particular        product, packaging, label, image, video, video frame, or        advertisement. These enhancements may make the content item more        effective for business purposes.    -   Adjust creative to better appeal to a particular user or        audience, or to achieve a particular business goal.        -   For example, a content item may transform differently if the            goal is 1) to generate more positive comments vs. 2) to            produce website conversions. The content item transformation            may also differ based on the target audience selected for            the content item, which may include an individual or a            larger group of individuals.    -   Rank and select which image from one or more images is the best        image to use for a particular purpose.    -   Rank and select which video or video clip or frame from one or        more videos, video clips, or frames is the best to use for a        particular purpose.    -   Score individual video frames    -   Identify the most relevant frames in a video or the most        resonant frames    -   If a content publisher uploads a content item and the system        1000 applies multiple transformations to the content item, the        system 1000 can show the content publisher which transformations        are predicted to have more positive outcomes. Thus, the system        1000 can return a plurality of transformed content items, along        with a ranking and scoring of the transformed content items.    -   Apply a recommended “filter” to a photograph. That is, the        system 1000 can automatically apply a “smart filter” to the        image.    -   Adjust the design elements of a graphic or man-made design.    -   Adjust one or more layers in a design file, e.g., Adobe        Photoshop's psd files or Illustrator files.    -   Apply a recommended treatment to a video or live video        broadcast.        -   For example, the system 1000 can determine that the NBA            audience would be more engaged by a video if it was            broadcast with a slight red hue. As a result, the system            1000 may dynamically apply such a filter to all incoming            video data as part of a video stream directed to the NBA            audience. The system 1000 may also apply the transformation            to an existing video file. This transformation may apply to            all footage or only a given segment, time frame, video            frame, or image of the video.    -   Automatically increase the number of marketing content assets        with more than one example that is highly (more) relevant to the        target audience, and likely to create a preferred business        outcome.    -   Automatically transform the content item based on real-time        data.    -   Create a set of computer-generated, pre-optimized content items        to use in A/B testing.    -   Create an optimal GIF.    -   Select a product photo for use on an e-commerce storefront.    -   Transform a product photo in order to improve product sales        performance on an e-commerce storefront.    -   Select an optimal video thumbnail for the purpose of increasing        video views or engagement.    -   Create an optimal video thumbnail for a video platform, website,        webpage, app, app screen, or social media feed.    -   Create a better image for a particular email message or blog        post.    -   Create a better image for use on a company's website or a        particular webpage or panel on a website.    -   Move a website panel or image(s) to another area on a website in        order to increase performance    -   Automatically re-write a social media post, subject line,        headline, body copy, or any text content used for marketing and        advertising purposes to be more effective with the target        audience.    -   Move, re-locate, replace or refresh imagery on a webpage or        website.    -   The system 1000 may transform text, for example by changing the        phrase “These apples are delicious” to instead read “These        apples are mouth-watering” Or “This fruit is delicious.”    -   Create a superior content item to use as a social media post or        within a particular advertising campaign. That is, for        single-purpose use OR ongoing use.        -   For example, many social networking posts are made, and then            published one time.            -   That is, most businesses and other content publishers                will not delete a post, and then re-post a new version                of it, or do duplicate posts, via their organic social                media channels.        -   A paid social networking advertising campaign can involve            multiple simultaneously running creatives, which can each be            swapped in and out over time.    -   Targeting the message of a content item to apply to the group or        individual person. For example, transformed content items can be        used to reach a particular target audience more effectively as        part of an advertising, marketing, customer experience, or        customer service initiative in an aggregated manner.    -   Transformed content items can be used to target at least one        user—in a personalized manner.        -   For example, the system 1000 can receive information            corresponding to the viewing habits of an individual over            the last 6 months on one or more platforms. The system 1000            can then take the content items the individual has engaged            with the most, and can learn from those content items            features and other aspects of the content items that appear            to be appealing to the individual. The system 1000 can then            apply machine learning to dynamically transform a new            content item to be more performant for the individual.        -   These content transformations can occur automatically in            real-time, or they may require the manual approval of an end            user. For example, some compliance-focused marketing            departments will not allow advertising creative to change            without proper approval.    -   The system 1000 can source a content item from an external        website or content repository (e.g., the content sources 1015)        or directly from users (e.g., the audience computing devices        1010 or the user computing device 1020), and can then perform        content optimizations on these content items dynamically,        depending on the goal of the content publisher.        -   Then, the system 1000 may recommend each of those content            items to be used at a particular time. These content items            may then be plotted into a content calendar or “flight            schedule” for advertisements. For example, the system 1000            can receive a raw image from a content publisher, can            transform the raw image to produce a transformed content            item, and can then recommended a particular time for            placement of the transformed content item. In some            implementations, the system 1000 may automatically transmit            or publish the transformed content item or served to the            intended user or target audience

The system 1000 may also generate one or more optimizations and thenrecommend that each new version is better for a respective audience. Forexample, given a first content item, the system 1000 can produce first,second, and third transformed content items (or any number oftransformed content items corresponding to respective transformations),and may also suggest that the first transformed content item is a goodfit for an audience of beer drinkers, that the second transformedcontent item is a good fit for Patriots fans, and that the thirdtransformed content item is a good fit for both beer drinkers andPatriots fans audiences.

In another embodiment, a user input may be captured which will informthe substance of the content item transformation. This user input may befacilitated by the system 1000. It may also be a derivative of anotherrecommendation that is generated by the system 1000. For example, theuser may select an option for adding more of a particular componentcolor (e.g., RGB color components) to their candidate content item. Thesystem 1000 can then perform a transformation on the content item thatmay include a re-styling procedure that adds more of the selectedcomponent color to the content item. The system 1000 may also modifyproximate colors within the content item such that the proximate colorsare converted to the selected component color. The user may also desireto insert a scenery background or an object into an image included inthe content item, as a result of a recommendation that may be producedby the system 1000. The system 1000 may then be configured to transformthe content item in order to adapt the content to the user's inputpreferences. In some implementations, a content item can be an imagethat may include a plurality of layers which are overlaid on one anotherto produce the complete image. In some such implementations, the system1000 may apply a transformation by altering any subset of one or morelayers of the image. Another subset of the layers may remain unaltered.Similarly, in some implementations, a content item can be a videoincluding a plurality of frames, and the system 1000 can apply atransformation by altering a subset of one or more of the plurality offrames, while another subset remains unaltered.

In some implementations, the system 1000 may automatically suggest newtransformations for the user to consider and opt into based on otherdata and learnings the system has made. For example, in someimplementations, the system 1000 can be configured to provide a userinterface allowing the user to see recommended transformation and todetermine whether one or more recommended transformations should beapplied to a candidate content item. In some implementations, the userinterface can be a graphical user interface (GUI). For example, the usermay be able to select a menu item corresponding to each recommendedtransformation using a pointing device, and may further use the pointingdevice to select whether or not a selected recommended transformationshould be applied to the candidate content item. The user may alsoindicate whether a system-provided transformation is acceptable orpreferable with his or her creative vision or business goal. In such anembodiment, the system 1000 may capture feedback data from the user, orfrom other systems utilized by the user, such as “thumbs up” or “thumbsdown” preference feedback. The system 1000 may also capture feedbackrelated to a number of business performance metrics. Further, the system1000 may incorporate this feedback data or a feedback loop based on thepostings of the user and the actual performance of the creativeexecution, content, or media placement. In one example, if a candidateimage that had been modified with a particular transformation notperform as well as expected once published, that transformation may be“blacklisted” from the user's account maintained by the system.Alternatively, the system 1000 may learn that that transformation isless effective in that particular context for the user. Thus, futurecandidate content items that are similar to the candidate content itemmay not receive that transformation. The system 1000 may also apply thattransformation in a dulled, weakened, or incremental, so as to testalternate variations of the transformation and its viability for theuser in other content items. In another example, the user may inputactual business performance data of a transformed content item. Thesystem 1000 may then use this data to inform subsequent recommendations.There are myriad ways in which the system 1000 may retrieve feedbackdata after making one or more recommendations. These means may be manualor programmatic, or learning-based such as via reinforcement learning,and the examples in this disclosure are not meant to limit the scope ofpossible implementations of the learning mechanisms. In general, thesystem 100 may use any means for receiving feedback related to theperformance of a published content item, including receiving suchfeedback from a user or collecting such feedback in an automatedfashion.

In another embodiment, the system 1000 may be used for influencer andmicro-influencer marketing use cases. For example, a brand, company, ormarketing or software vendor may use the system 1000 to optimallytransform a content item such as an image, text, or video content thatwas provided from the brand or the brand's advertising agency to aninfluencer for promotional purposes. In this example, the brandedcontent to be shared by the influencer may benefit the brand and may beoptimized by the system 1000 to appeal to an audience that includes thatparticular influencer's followers or another target audience in whichthe influencer may have reach and authority. In another example, thesystem 1000 may transform user-generated content that may have beenoriginally produced by the influencer or microinfluencer or anotheruser. In this example, the system 1000 could automatically apply atransformation to the content that could be shared or promoted by theinfluencer, which may or may not be on behalf of a brand or company. Thecontent need not have been advertising content in nature. An influencerneed not be an actual celebrity figure. The influencer, or individualconsumer, may use the product on their own behalf in order to increasethe impact of their own messages and content and thereby increase theirinfluence, authority, and followership. The system 1000 may also beutilized by an ordinary person who is interested in self-promotion ofhis or her content, public persona, or social media accounts. There aremillions of individuals who want to build a larger online following andcultivate more engagement per content posting.

In another embodiment, transformations produced by the system 1000 maybe used in the creation of a new file type, which may provide a uniqueadvertising or marketing experience to a user. For example, a new filetype may correspond to any video, audio, or text based content (orcombinations thereof) that can be presented to an audience through anytype of medium. This file type may automatically update its contentsaccording to the layer-based transformations as described in thisapplication or based on other contextual information.

In another embodiment, the system 1000 can implement customtransformations or “filters”. That is, in the future it is conceivablethat new visual design treatments, methods, enhancements andtransformations may be computerized such that the transformation of acandidate content item may be unique to any candidate content itemcurrently on the Internet or on the World Wide Web.

The system 1000 may also be utilized in the context of Adobe CreativeCloud design projects. For example, Adobe Photoshop is a softwareprogram used by creative professionals to design graphics and visuals,edit photos, and work on various forms of advertising content, digitalart and graphics. A design file for a given image can be converted intoan image format such as PNG or JPG. Photoshop also allows for files tobe saved as Photoshop Document Files (PSDs). PSDs can be used topreserve editing capabilities within Photoshop such that an image can bemanually edited at a later time by a designer). As a result, it iscommon practice for PSDs to be shared among design professionals so thatdesigns can be edited, re-worked or otherwise changed by an individualwith knowledge of Photoshop. In some implementations, the system 1000can receive a candidate content item formatted as a PSD file, and canselect and apply transformations for the candidate content item that arecompatible with the PSD file.

In the system 1000, the transformations disclosed can be performedautomatically, rather than manually by a human. Therefore, using thesystem 1000, it is not necessary to rely on designer to make changes tocontent items, which may be inherently subjective according to thedesigner's preferences. Within some content editing tools such asPhotoshop, designers can utilize “layers” for graphics work and designprojects. Layers separate different elements of an image or video. Indigital image editing, designers use layers to work on individual partsof an image, while not affecting the other parts of the image. Forexample, consider an image of a girl standing outside in a gardenlooking down at a group of bright red strawberries. In this example, theimage may have four layers so that each of the components of the imagecan be treated as separate entities in the overall image file, which canbe formatted as a PSD file. In some implementations, each layer may beformatted as a PNG file. For example, the image of red strawberries maybe in one layer of the design, the plants in the garden may be containedin a second layer, the image of the girl may be in a third layer, andthe background image of a sky may be contained in a fourth layer. Otherelements, such as headline text (copy), a brand logo, a call-to-action,etc. may be contained in separate layers.

It is possible that even more layers are added to separate subtle designelements that were added. In theory, a design file could have hundredsor thousands of layers, depending on the desired complexity of thedesign. In some implementations, the system 1000 may perform “PSDparsing,” by automatically separating a PSD file by layer intoindividual image files (e.g., PNG or JPG). In doing so, the system 1000can generate a unique transformation for each layer independent of theother image layers in the overall file. The system 1000 can also applytransformations to the overall PSD file. In one example, if the entirePSD is converted into one PNG file, the system 1000 can apply atransformation to that PNG file as a whole. Continuing the exampleabove, a transformation may affect the visual appearance of the girl,the strawberries, the sky background, the garden, or any combinationthereof. In another example, if the PSD file is separated into fourseparate PNG image files, each for one of the four image layers of thedesign, then the system 1000 can select a unique transformation for eachlayer. In some implementations, one or more layers may receive notransformation at all. In some implementations multiple elements of animage may exist in a single layer In some implementations, if a user isan employee of a consumer brand that sells orange juice, the user of thebrand marketing team may not want to make any alterations to the visualpresentation of the bottle of orange juice The color, the appearance ofthe label, the brand font, etc. may be protected under a style guide orbrand guidelines. Therefore, the system 1000 can allow users to applytransformations only to the layers, or individual elements, of the imagethat the user desires to be modified. The system 1000 can thus allow theuser to tag individual layers for transformation. For example, a usermay tag a layer containing a product image of orange juice as a layerthat should not be modified by the system. In this case, the systemwould treat this layer as a separate image and not perform anytransformations to it according to the user's preferences.

In another embodiment, the user may select the type of transformationthat can be performed to the layer containing the image of a bottle oforange juice. For example, the user may not want the visual compositionof the bottle to be altered in any way, but he/she may want the systemto replace the bottle with another image of a bottle that is more likelyto resonate with the target audience. Therefore, the system 1000 mayperform a “refresh” operation for that particular layer, in which thenew image of the orange juice bottle would be more likely to resonatewith the audience and also improve the score (and potential success) ofthe overall image. Alternatively, the user may allow for atransformation to the layer containing the bottle that moves the bottleinto a different location, or adjusts the size of the bottle, within theimage. In another example, the transformation that moves the bottle to amore optimized location in the image, or resizes the bottle, may beautomatically deployed by the system 1000. In another example, atransformation may be applied to the layer containing the skybackground, which has the effect of brightening the color blue. In yetanother example, transformed images from a design file or video may beturned into a GIF format for use.

Many variations of layer-based transformations are possible in thepresently disclosed system and the descriptions above are not meant tobe limiting in scope in any way to the potential applications of thesystem 1000. In some implementations, the system 1000 may automaticallydetect and categorize layers of an image based on the contents itidentifies while processing the image layer(s). For example, the system1000 may apply computer vision and object and pattern detectiontechniques. These techniques may distinguish between a background, acohesive object, sets of objects, etc. The system 1000 may also detectand categorize brand elements, such as a product packaging or a brandlogo, and make a determination about which transformations should beapplied to each layer or feature of a content item. In someimplementations, the system 1000 may make a determination to apply zerotransformations to one or more layers. The system 1000 may also, basedupon its analysis of the layer's contents, adjust the intensity of atransformation or otherwise create and apply a new transformation to oneor more elements of the layer's contents (e.g., add a special treatmentto an object contained in a layer) in order to improve the contents ofthe layer. Transformations may also be “chained” together, such that thesystem 1000 can link multiple transformations together to be applied toa given content item or to a given layer within a content item.

The system 1000 may also be utilized in a similar manner with otherdesign software, photo editing, or video production software not limitedto Photoshop. In an example, similar operations to those disclosed abovefor graphics and design files may be applied in the context of videofiles (e.g. MOV, MPG, AVI, MP4, etc.) The system 1000 can parse a videointo images, calculate scores (for example, scores per frame) and applya transformation effect(s) during a time period, or number of frames, orto an individual frame in a video. The system 1000 may also determinethe most relevant or engaging frames in the video. The system 1000 mayalso layer an effect on top of a video as a transformation. For example,if a user of the system 1000 determines that the first 3 seconds of avideo represents the most critical time frame to capture a viewer, oraudience's, attention, then the user may desire to only transform thosefirst three seconds. In such an embodiment, the system 1000 may apply aset of transformations to the contents of the first three seconds ofvideo and apply zero transformations to the video after the first threeseconds. In other embodiments, the system 1000 may apply transformationsto the entire video file, or to distinct segments, time periods, orindividual frames of the video file. The system 1000 may also identifycollections of frames with similar contents and apply the sametransformation(s) to those frames. For example, if the candidate contentitem is a video of a man shooting a basketball, each frame of that shotmay be transformed with the same transformation, e.g. applying a slightchange to the hue or saturation of each successive frame. In analternate embodiment, the transformations of related content may varydepending on the determination of the system 1000 or by user preference.For example, if the user determines that the current set oftransformations applied to a particular time frame of the video do notlook aesthetically pleasing, then he or she may perform an alternatetransformation or instruct the system 1000 to adjust the way it isapplying transformations to that video clip. The user may also want asingular video frame to be transformed. In such an example, the system1000 may convert the video frame to an image file, such as a JPG file,and perform transformations similar to imagery. Like transformation forimages described above, transformations for videos also can be informedby the user's preferences and business needs, and may have a wide rangeof potential applications. In yet another embodiment, the system can beutilized with Adobe Illustrator (AI and EPS files) to performtransformations. For example, the system may move, align, distribute,and transform objects in Illustrator. The system may be utilized to edittext or other elements within the graphic or perform transformations tothese any of these elements.

In another implementation, the system 1000 can be utilized toautomatically evaluate imagery on a webpage, webpages, or website. Inthis example, a user may provide a URI to a public website and thesystem 1000 can crawl the user-provided URI, save any images or othercontent items found on the site, and evaluate each content item. In suchan embodiment, the system 1000 can return to the user a variety ofinformation. For example, the system 1000 may determine the averagescore for each image, determine the top images, and determine how manyimages (and which specific images) are underperforming among a targetaudience. This information can then be shown to the user. Thus, the usercan be able to quickly evaluate content items and take correctiveaction. In another example, the system 1000 can recommendtransformations to the underperforming images. Similarly, the system1000 may determine transformations that may make the website'shigh-performing image assets even stronger. In another example, thesystem 1000 may recommend a transformation which involves moving animage from one area, or page, on the site to another area, or page onthe site. Another transformation may involve moving one website panel toanother area or page on the site, or involve moving a particular imageon a panel on a website to another panel on a website. In someimplementations, the system 1000 can evaluate imagery or other within amobile application or desktop application. The above-mentionedembodiment may be applied in a variety of contexts where contents may beevaluated and “refreshed” in order to improve performance.

As described above, the system 1000 can be used to transform varioustypes of content items, which may include images, audio, video, text,and other features. Various examples of types of content items that thesystem 1000 can be used to transform or optimize are described below. Itshould be understood that the following examples are provided by way ofillustration only, and not by way of limitation. One of skill in the artwill appreciate that the types of content described below are merelyexemplary, and that other types of content items also may be transformedby the system 1000 without departing from the scope of this disclosure.

Example Content Item Types that can be Transformed:

The following list includes social, digital, print, broadcast,traditional media, etc. For digital media, content items can beimplemented, for example, as static creative, flash creative, rich mediacreative, or in other forms:

-   -   Photographs    -   Stock photos    -   Landing page, webpage(s) and website images    -   Social media posts    -   Email message and email newsletter images    -   Television and broadcast advertising    -   Video, video clips, or video feeds    -   Live video broadcast    -   Video thumbnail image    -   Display ads    -   Print media    -   Magazines    -   Books and ebooks    -   App screens    -   Leaflets    -   GIFs and video files    -   MP3s and music files    -   Newspaper    -   Banners    -   Billboards    -   Posters    -   Brochures    -   Cards    -   Flyers    -   Booth and trade show displays    -   Online advertising    -   Sponsored content    -   Organic content    -   Cell phone and mobile advertising    -   Website graphics    -   Mobile content    -   Mobile ads    -   Mobile coupons    -   Downloadable coupons    -   Notifications    -   Freestanding inserts    -   Look books    -   Print circulars    -   Point of sale advertising    -   Direct mail advertising    -   Product placement    -   Event and sports sponsorships    -   Menus    -   Guerilla advertising    -   PPC search ads    -   Display ads: static, video, cinematographic, flash, pop-up and        mobile    -   Social ads: Facebook, Twitter, YouTube, Instagram, Pinterest,        Snapchat, LinkedIn, etc.    -   Interactive video advertisements and clickable advertisement        customizations    -   Augmented reality contents and experiences    -   Virtual reality contents and experiences    -   Interior design-related visuals    -   Architectural/building visuals    -   Merchandise designs    -   Product and material designs    -   Fashion designs/clothing and apparel designs    -   Fine art and commercial artwork    -   Design files (PSDs, AI, EPS)    -   Trade promotions    -   Coupons    -   Cartoons/Comics    -   Digital Art    -   Product ad design    -   Blog    -   Search ads    -   E-commerce product images, product pages/galleries, and displays    -   Store design    -   Virtual store design/Online store    -   Mobile store design/Mobile storefront    -   Carousel advertisements and other multi-dimensional content        types    -   Infographic    -   Graph/Chart/Diagrams    -   Illustration    -   Silhouette    -   A “creative refresh” for an advertising campaign    -   A “campaign refresh” for an advertiser    -   Video end card    -   Bumper    -   Animation    -   Dailies    -   On-figure images    -   Still life images    -   Holiday creative    -   On-location shoots    -   Studio shoots    -   3D art    -   Product labels    -   Sports merchandise    -   CSS    -   HTML, XHTML, HTML5    -   XML

As disclosed herein, the system 1000 may store a huge corpus of contentitems from sources such as the Internet and a variety of both online andoff-line content sources from which “audiences” are defined for thepurpose of analysis and transformation. In the below example, contentitems can be harvested from any of the audience computing devices 1010,the content sources 1015, and the user computing device 1020. Forexample, some of these components may represent public social mediaaccounts that are part of a defined audience. Generally, content itemscan be harvested by the content item harvesting module 1030.

In some implementations, the system 1000 can implement advancedartificial intelligence and machine learning techniques, such asconvolutional neural networks and generative adversarial networks, toacquire and analyze content items. However, it is feasible that thesystem 1000 could use other collections of algorithms to accomplish asimilar result. In an example, the content item harvesting module 1030collects content items from a harvest of social media accounts ofmembers of a defined target audience. These content items can bereferred to as harvest content items.

In some implementations, the content item harvesting module 1030 can beconfigured to collect harvest content items that match one or morecriteria. The criteria can be selected, for example, based on the targetaudience. For example, the content item harvesting module 1030 cancollect content items having textual content (e.g., metadata, captions,hashtags, etc.) relevant to the target audience. In someimplementations, the content item harvesting module 1030 can acquiresuch content items through an API of a website or social network. Forexample, if the target audience is sports fans, the content itemharvesting module 1030 can search for content items (e.g., images orvideos) that include the hashtags “basketball,” “football,” or“baseball” on one or more social networks.

The system 1000 can analyze the harvest content items, and can evaluateand rank them based on a number of characteristics including userpreference data (e.g. volume of engagement on social media). In someimplementations, the performance metric ranking engine 1035 can carryout the steps of ranking the harvest content items relative to oneanother, for example by generating a numerical score for each harvestcontent item that represents the performance of that content item amongthe target audience for a given performance metric. The higherperforming content items (e.g., the content items with higher scores)with regards to the user preference data and/or audience data can beharvested by the content item harvesting module 1030 from one or moredata sources. For example, if a user wanted to design the best image ofa cup of coffee, the content item harvesting module 1030 could gatherimages from social media accounts, public search engine results, acontent library, a stock photo site, a content repository associatedwith a marketing or advertising platform, websites and news feeds, etc.,all of which may correspond to the content sources 1015.

The system 1000 can process the harvest content items to determine whichtransformations, or categories of transformations, the visual featuresof each harvest content items represent or are reminiscent of. In someimplementations, the system 1000 can implement a neural network toprocess the harvest content items. The matching criterion manager 1040can then process the harvest content items to produce a matchingcriterion for each harvest content item that can be used for comparisonwith other content items at a later time. In some implementations, thematching criterion manager 1040 may implement a neural network toprocess or classify the harvest content items in this manner. Acandidate content item can be provided by a user of the computing device1020 for transformation by the system 1000. The system 1000 can processthe candidate content item, for example using the second neural networkreferred to above, to identify one or more harvest content items, visualattributes, or categories, that match most closely with the candidatecontent item. In some implementations, the system 1000 can achieve thisby comparing the harvest content items themselves to the candidatecontent item, for example via the matching criterion manager 1040. Insome other implementations, the system 1000 can compare characteristicsof features of the harvest content items, such as the matching criterionaspects, with those of the candidate content item or to the candidatecontent item itself.

In some implementations, the system 1000 may evaluate more than onecandidate content item at a time. For example, the user of the usercomputing device 1020 may provide two or more candidate content items tothe content evaluation system 1005, and the content evaluation system1005 can evaluate the two or more candidate content itemssimultaneously. In some implementations, the performance metricpredictor 1060 can be configured to predict performance metrics for eachof the candidate content items for a given target audience, which mayalso be selected by a user of the user computing device 1020. Therecommendation module 1045 can then generate a recommendation for theuser indicating that the candidate content item most likely to beperform well with the target audience should be published. In some otherimplementations, the recommendation module can provide a recommendationof a transformation to be applied to one or more of the candidatecontent items. The transformation can be selected to increase alikelihood that the candidate content item will perform well with thetarget audience. In some implementations, more than one transformationcan be applied to one candidate item at a time. For example, candidatecontent item A may receive transformation X and transformation Y, ortransformation X and transformation Z, or transformation W andtransformation X, etc. There can be hundreds, thousands, or millions ofpossible combinations for evaluating the transformation matrix anddetermining the best content assets. In another embodiment, candidatecontent item A may be transformed by transformation X (producing contentitem A-X), and then be separately transformed by transformation Y(producing content item A-Y). Thus a user is capable of evaluating eachtransformed image against the other transformed images (or against otheravailable images in the content library, which may or may not have beentransformed by the system). This process too can create thousands andthousands of iterations.

Thus, the recommendation module 1045 can provide a best-fittransformation recommendation for the purpose of improving a selectedperformance metric of the candidate content item. In someimplementations, the selected performance metric may be related to atarget audience, as described above. The performance metric may also bea numerical score that is provided by the system 1000 (e.g., by theperformance metric predictor 1060) as an objective evaluation of thequality of the content item for the particular audience. For example,the system 1000 may generate a score for a transformed content itemindicating its likelihood of achieving a desired outcome (e.g.,succeeding as defined by one or more performance metric) with respect toa target audience. In some implementations, the system 1000 can generatea scaled score for content items based on a predetermined scale. Thus,the system 1000 can generate a score for a content item on a scale ofzero to one, one to ten, one to 100, or any other scale. In some otherimplementations, the system 1000 can generate a score for a content itemas an absolute number based on any of a variety of factors impacting itslikelihood of performing well according to a performance metric. Thesystem 1000 can generate a percentile or percentage-based ranking in asimilar manner. In still other implementations, the system 1000 cangenerate a non-numerical score for a content item. For example, thesystem 1000 may generate letter scores of A-F for a content item, with ascore of “A” corresponding to the highest probability of the contentitem performing well. Thus, the transformation recommendations providedby the system 1000 can vary based on the candidate content item. In someimplementations, the system 1000 can provide more than onetransformation recommendation for the candidate content item.

Content scores provided by the system 1000 can also be compared againstother known benchmarks. For example, the user may compare the score ofan image against other images the user has previously used. Thisbenchmark may be helpful to the user in comparing current or pastcampaigns. If a new content item scores a 55.4, but the average contentitem of the user's past campaigns (or existing content library) is a32.6, then relatively speaking the content item scored 55.4 may beexpected to a better content item (e.g., a higher-performing contentitem according to one or more performance metrics). In another example,the system 1000 can evaluate an audience benchmark. For example, if acontent item scores a 55.4 out of 100, that may be perceived as a lowscoring content item on its face. However, if that score is in the top99^(th) percentile of all content that the target audience consumed thatweek, then that may be determined to be a strong content item amongother creatives that audience had been shown. This superior contentquality may lead to greater awareness, memorability, and recall among atarget audience. Similar types of score comparisons can be derived anddisplayed for image categories, content categories, content repositoryor libraries, marketing channels, competitor, competitor sets, industry,file type, etc. a user of the system 1000 who performs competitorbenchmarking can use the scoring mechanism provided by the system 1000as an objective measure with which to evaluate a campaign and/or acontent item against the campaign and/or content items of competitors.The user could also apply these scores in determining comparisons ofwebsite imagery, social media imagery, etc.

In another embodiment, an individual content item can receive multiplenumerical scores, e.g. one for each of the user's target audiences. Thatis, a content item can be scored for more than one target audience at atime. For example, if a user uploads an image of a bicycle to thesystem, that image may score a 45 among Men, a 56 among Men in Boston, a45 among sports fans, a 60 among bikers, a 97 among female sports fans,etc. Thus, content items can be scored within demographic targetaudiences, brand audiences, competitor audiences, interest andpsychographic audiences, behavioral audiences, conversation groups (i.e.people who are talking about a topic or keywords), or any combinationthereof, or any type of target audience that is of interest to the user.For any given content item, a user can evaluate how the asset is likelyto fare with different groups of people, customers, or prospectivecustomers. Multi-audience scoring evaluations such as these can beperformed while the content is being designed, after the content iscreated, before it is published, or after the content is published.

In some implementations, the user can select a transformation to applyto the candidate content item, for example, via a user interfaceprovided by the system 1000. However, in some other implementations, thesystem 1000 can be configured to apply the transformation automaticallyto the candidate content item without a user input. By applying thetransformation, the system 1000 generates a transformed content item.The system 1000 can return the transformed content item to the user as anew visual or media asset, along with one or more projected performancemetrics or scores generated by the performance metric predictor 1060 orthe performance metric ranking engine 1035. In some implementations,multiple versions of the candidate content item can be returned to theuser, each with a different candidate transformation, and each candidatetransformation may be associated with a respective predicted performancemetric.

It should be understood that, in some implementations, the candidatecontent item can be uploaded directly into the system 1000 by the usercomputing device 1020. In some implementations, the system 1000 cancause the candidate content item to be uploaded. In some otherimplementations, the system 1000 can retrieve the candidate content itemfrom a database, via browser extension, or via API connection to anotherapplication, website, or content repository, or via desktop client,mobile app, or chatbot, which may be represented in FIG. 10 by thecontent sources 1015 or the audience computing devices 1010.

In another embodiment, the system 1000 can allow a user to evaluateimages outside of the platform while they are on the Internet. This maybe particularly useful when the user is on other web-based business andmarketing application software, or while the user is on a socialnetwork, a website, or on stock photography and other image-rich domainsor competitor properties. In one particular example, which is not meantto be limiting, the system 1000 may provide the user with access to abrowser plugin (e.g. for Google Chrome), which is linked to an accountof the user maintained by the system 1000. In some implementations, sucha browser plugin can be provided to the user by the GUI generationmodule 1075. This could also be achieved with an application integrationon certain websites or with certain web-based software providers. In thecase of a browser plugin, for example, the user would have the option toright-click-analyze an image (similar to how users on the web canright-click “save as” for an image or link). This function can allow theuser to quickly assess the quality and viability of a particular imagethat the user found on the Internet. For example, if the user is tryingto choose between ten images of sunsets to purchase from a stockphotography website, the user can select each one and evaluate theimages with respect to a target audience using the system 1000, in orderto determine a likelihood that the target audience will find itengaging. The evaluation or score provided can thus allow the user tomake a more informed decision.

Additionally, the plugin can be configured to detect, grab, and displaya series of images or other content items found on a webpage that a useris currently visiting. These images may be shown to the user in anoverlaid screen and displayed as a “gallery” of various images. In thisexample, the user can utilize the system functionality to evaluate,rank, and score all of the images simultaneously (or in sequentialfashion by selecting the individual images the user wants to beevaluated) on a page against a particular target audience, or selectionof target audiences. This can allow the user to quickly compare manyimages and make a more rapid determination of the best image for his/herparticular purpose. The scores may also be displayed to the user upon animage hover as the user's cursor “hovers” on top of the image inquestion on a webpage, for example. The user may also use this tool andsimilar tools made possible by the system 1000 disclosed herein whenevaluating images on their websites, social properties, or othercompetitor's properties in order to determine when certain optimizationsare available. There are many such use cases and the examples above arenot meant to limit the scope of potential ways that users may employthis technology.

In another example, the GUI generation module 1075 may provide a userwith access to a user interface via an application, such as anapplication executing on a mobile device (e.g, the user computing device1020). The application may be linked to an account of the usermaintained by the system 1000, similar to the browser plugin describedabove. The application may provide a user interface that allows the userto analyze an image or other content item stored on the device thatexecutes the application. For example, the user could make use of theapplication to quickly evaluate a photo stored in a photo library on theuser's computing device or content management system or cloud storagesystem. In some implementations, the application can transmit selectedcontent items from the user computing device 1020 (or from anotherlocation) to the system 1000 for evaluation, and can return the resultsof the evaluation to the users computing device 1020. In someimplementations, the system 1000 can evaluate more than one item at atime, thereby allowing the user to evaluate any number of photos orother content items stored on the user's computing device to determinewhich content items are likely to be most effective with a targetaudience. For example, the user can upload a batch of content items tothe content evaluation system 1005 for simultaneous evaluation. Such animplementation may be particularly valuable for users who frequentlypublish content (e.g., photos or videos) stored on a mobile computingdevice, such as a smartphone, as it would enable these users to moreeasily determine which content items are likely to be most popular amongtheir followers or among any other target audience.

In some implementations, the system 1000 can provide the user with avoice-activated interface. Such functionality can be provided alongwith, or separate from, any of the GUIs discussed above, such as a webbrowser extension of a standalone application. Generally, avoice-activated interface can allow a user to provide voice commands(e.g., via a microphone of a user computing device) allowing the user toperform any of the functions discussed above in connection with thevarious GUIs described above. For example, the system 1000 can allow auser to select or publish a content item, such as a transformed contentitem, via a voice command. In addition, the system 1000 can beconfigured to support other voice-activated commands. In someimplementations, the system 1000 may be configured to process voice datacorresponding to natural language. Thus, a user may be able to interactwith the system 1000 by issuing voice commands such as “show mealternate examples,” “transform this image,” “show me an image examplewith a different X,” “what are my top colors,” “show me images that areengaging my audience,” “rank these images,” “resize my logo,” “remove mylogo,” change color X to color Y,” “change the background,” “transformlayer X,” “what is the score of this image,” “how does this image scorewith audience X”, “how does this image score against a competitor,”“optimize this image,” “build me a mood board,” “find me the best imageof X,” etc.

As discussed briefly above, the harvest content items are not limited tobeing harvested from social media accounts. For example, in someimplementations, the system 1000 can harvest images from anywhere on theWorld Wide Web or from any accessible database or data store. The system1000 could also harvest content items from an advertising platform thatmaintains a stored record of past creatives, along with the relevantperformance data, that were used in campaigns, which may have been usedto target a particular audience in a campaign at a particular time witha certain set of characteristics, budget, copy etc. The harvest contentitems may also be part of a dataset that is not owned or maintained bythe user.

As described above, the system 1000 can be used to transform contentitems to achieve a variety of performance metrics. Various examples ofperformance metrics that the system 1000 can be used to optimize for aredescribed below. It should be understood that the following examples areprovided by way of illustration only, and not by way of limitation. Oneof skill in the art will appreciate that the types of performancemetrics below are merely exemplary, and that the system 1000 can beconfigured to achieve other types of performance metrics than thosedescribed below without departing from the scope of this disclosure.

Sample Content Performance Metrics:

-   -   Impressions    -   Clicks    -   Engagement (e.g., social media engagement)    -   Number of followers    -   Follower growth rate    -   Shares    -   Comments    -   Time on site    -   Time viewing advertisement    -   Sales    -   Sales lift    -   Revenue growth    -   Repeat sales    -   Conversions    -   Cost per lead    -   Cost per click    -   Cost of customer acquisition    -   Churn rate    -   Customer retention rate    -   Content Downloads    -   Inbound links    -   Lead quality score    -   Relevance score    -   Cost per thousand (CPM)    -   Lead volume    -   Subscribers (blog, newsletter, email, etc)    -   Website traffic (e.g., monthly)    -   Unique website visitors    -   Customer lifetime value    -   Lead to sale conversion rate    -   Cost per order    -   Net promoter score    -   Profitability    -   Referrals    -   Frequency    -   Fatigue    -   Click through rate    -   Number of view views    -   Cart size    -   Order size    -   Average purchase value    -   Response time    -   Market share    -   Brand recall    -   Ad recall lift    -   Market penetration    -   Household penetration    -   Memorability    -   Brand association    -   Qualified leads (e.g., per month)    -   Net profit margin    -   Gross margin    -   Monthly recurring revenue (MRR)    -   Employee happiness    -   Social media sentiment    -   SEO keyword ranking    -   SEO traffic    -   Social media mentions    -   Call abandonment    -   Resolution rate    -   Customer satisfaction score    -   Dwell    -   Churn prevention    -   Unit share    -   Relative market share    -   Brand penetration    -   Unit share    -   Awareness    -   Top of mind    -   Purchase intent    -   Likeability    -   Loyalty    -   Willingness to refer/provide referral    -   Share of voice    -   Net reach    -   Gross ratings points    -   Page views    -   Frequency response    -   An attraction metric    -   Unsubscribe rate    -   Uniqueness    -   Resonance    -   Relevance

The system 1000 may optimize content items against any criteria ormetadata that is related to the content being processed by the system1000. For example, the system 1000 can examine a set of webpages visitedby one or more users, and can apply a weight to the “value” of eachcontent item on those pages based on the amount of time the user stayedon the page. In another example, the system 1000 can weight each contentitem by whether or not that content item led to the user completing someaction of interest (e.g., filling out a form or adding a product to ashopping cart). The system 1000 can also be configured to place a highervalue on content items that have received more viewer activity,comments, social media engagement or have been “Pinned” by users mostoften with a Pinterest-like plug in.

In some implementations, to make a content item more engaging, thesystem 1000 can be configured to determine how unique the content itemis relative to other content items viewed by the target audience, or totransform the content item to increase its uniqueness. For example, thesystem 1000 can extract various features from the candidate content itemas well as from a set of harvest content items. In some implementations,the system 1000 can examine the frequency of the features of thecandidate content item within the sample of harvest content items, andcan determine a score of the candidate content item's uniqueness. Forexample, if the features of the candidate content item appearinfrequently in the harvest content items, the system 1000 can determinea relatively higher score relating to the uniqueness of the candidatecontent item. In some other implementations, the system 1000 can beconfigured to generate a vector representing a plurality of features ofthe candidate content items, as well as vectors representing thefeatures of each of the candidate content items. The system 1000 canthen determine the uniqueness of the candidate content item based ondistance between the vector for the candidate content item and thevectors for the harvest content items. It should be understood that, insome implementations, the system 1000 can use digital representationsother than vectors to calculate the uniqueness of a candidate contentitem in a similar manner to that described above. In someimplementations, some of the features examined by the system 1000 todetermine the uniqueness of a candidate content item may be featuresthat are not readily apparent to a human consumer of the candidatecontent item.

In some implementations, the system 1000 can evaluate a content itembased on a variety of performance metrics as described above, includingone or more performance metrics relating to engagement. Generally,engagement can refer to any type or form of interaction with a contentitem by an audience member. For example, engagement can relate to anumber of views, a number of shares, a number of likes, a number ofcomments, or any other number of user interaction events experienced bythe content item among the target audience. Thus, engagement can be ameasure of a level of interest that the target audience has in a contentitem relative to other content items. In some implementations,engagement may relate to resonance or relevance of a content item amongthe target audience.

In some implementations, the system 1000 may use audience activity dataon a social network to inform the transformation of candidate contentitems for use in an advertising campaign. Alternatively, the system 1000may use historical response data to one or more campaigns. For example,the performance measurement module 1065 can be configured to track oneor more metrics relating to a response of members of a target audienceto various harvest content items. Metrics relating to audience responsecan include, for example, clicks, views, sales, etc. These campaigns mayhave targeted the same or a similar audience as the one that iscurrently being targeted now. The system 1000 may examine one company'spast campaigns, e.g. the performance of a coffee seller's advertisingcreative towards an audience made up of coffee drinkers. In someimplementations, the system 1000 may examine a competitor or competitorset of advertising creative performance (e.g., the response rates to acompeting coffee seller's last two advertising campaigns on a particularplatform). The system 1000 may also combine multiple audience oradvertising measurement datasets in order to inform the transformations.The system 1000 may also learn from the performance data of othercompanies who are not competitors, but who are trying to market and sellto the same audience.

After performing these transformations, the system 1000 can return newtransformed versions of the candidate content items. The user may thenchoose to download, share, edit, or publish the transformed contentitems, for example in an advertising campaigns. Once a campaign is live(e.g., currently in progress), the system 1000 can be configured tosuggest and/or enact new transformations to the content items of thecampaign based on new data as it becomes available. The data used toinform the new transformation may be audience activity data from one ormore digital sources, such as a social network or website or mobile orweb or desktop application software, or it may be based on the responserates to the present creative assets, or to any other external datasetsthat can be used as “signals” as to inform the selection of one or moretransformations. For example, the system may integrate with a websiteanalytics software and may recommend an image transformation in order toincrease new visitors to a company's website. The amount of traffic tothe company's website can be tracked in real-time and thus the system1000 can use the traffic information to determine changes to thecreative in order to optimize the company's performance against the goalof maximizing new website traffic. Any number of business, marketing, orsales goals may be calibrated to the system 1000 in such a manner. Thesystem 1000 may enact new transformations based on a user's desire toreach a new target audience, or desire to refresh content previouslypublished to a current target audience, the process of which may involveproducing one or more variations of previously transformed content.

As described above, the system 1000 can be used to transform contentitems in a variety of ways. Various examples of transformations that thesystem 1000 can perform on candidate content items are provided below.It should be understood that the following examples are provided by wayof illustration only, and not by way of limitation. One of skill in theart will appreciate that the types of transformations below are merelyexemplary, and that the system 1000 can be configured to perform othertypes of transformations than those described below without departingfrom the scope of this disclosure.

Sample Types of Content Item Transformations:

-   -   Word choice, such as synonyms.    -   Sentence structure, punctuation, syntax    -   Language    -   Adding keyword, phrase, or hashtag    -   Adjust word order or sentence order    -   Adding, changing or removing an emoji or symbol    -   Adjusting the tone or sentiment of the content    -   Move the location of an image on a webpage    -   Replace the image on a webpage    -   Adjust the text on a webpage    -   Move the location of text on a webpage    -   Edit, move or replace a panel on a webpage    -   Edit, move or replace the contents of a panel on a webpage (e.g.        an image on a panel)    -   Adjusting color histogram of the image, RGB and HEX value        modifications    -   Manipulate HSV color space. Adjust the image along the color        spectrum, e.g. slightly increase HSV values    -   Adjusting the saturation of the image    -   Adjusting the composition of the image    -   Adjusting the layout or balance of the image    -   Manipulating the edges of the image    -   Adjusting the symmetry of the image    -   Changing, moving or altering the focal point of the image    -   Pixelate a certain area or feature of the image (pixelization        effect)    -   Emboss the image    -   Apply Photoshop photo effects    -   Apply a negative effect    -   Apply an infrared effect to an image    -   Apply an X-Ray effect    -   Apply a bokeh effect    -   Apply a blooming effect    -   Apply a vignetting effect    -   Adjust the framing of the photo    -   Add a border to the image or a feature/element of the image    -   Enhance the edges in the image    -   Invert the image    -   Invert the colors of the image    -   Adjust shading/shadow or lighting    -   Adjusting the amount and location of white space in the image    -   Adjusting the resolution of the contents    -   Adjusting the hue, tint, or tone of the contents    -   Adding a specific image feature, such as an object    -   Restyling of 1 or more image features, such as objects,        products, scenes, visual elements    -   Replacing of 1 or more image features, such as objects,        products, scenes, visual elements    -   Applying an overall stylistic filter to the entire image    -   Applying a gradient, accent, or overlay to the image or to a        specific area of the image.    -   Add a logo or watermark    -   Add a call-to-action    -   Resize the call to action    -   Move the location of logo or branding features    -   Adjust the color, font or style of the headline, text or        call-to-action    -   Move the location or position of an image feature, such an        object, product, call-to-action, button, etc.    -   Adjust the lighting or brightness    -   Adjust the scene (e.g. beach, living room, kitchen, restaurant,        football stadium, etc)    -   Change the background color or scenery    -   Adjust the time of day (daytime/nighttime)    -   Adjust contrast of the image    -   Adjust the shade and shadow    -   Apply a transformation that makes the image to look like a        cartoon    -   Apply a transformation to make the image look like an        illustration or artwork    -   Add infographic-like elements to the image    -   Insert new text into the image    -   Turn an image featuring a single product into an image of a        product collage    -   Adjust the size and location of the headline text, or main text,        etc.    -   Adjust the texture of the image    -   Adjust the font of text in an image    -   Add a pattern to the contents    -   Add a shape to the image    -   Adjust the shape of certain contents    -   Manipulate the edges, contours, and boundaries of the image        features    -   Turn the image to be gray scale    -   Applying a transformation to one layer in the image design file    -   Applying a transformation to a collection of layers in the        image's design file    -   Adjust the forms, lines and angles of elements of the image    -   Adjust the spatial context or spatial relation of items in the        image    -   Remove one or more layers in the image    -   Move or replace contents in one or more layers in the image    -   Adjust the size of objects in an image    -   Adjust the sharpness or blurriness of a photo    -   Apply a blur effect to a layer in an image    -   Applying a negative effect or inverting certain colors in the        visual content    -   Crop/re-crop an image (for example, to fit the optimal        dimensions for a certain platform, such as a Google banner ad,        Facebook ad, etc.)    -   Increase the size of the text    -   Shrinking the size of the text in an image    -   Adjust the highlights in the image    -   Apply a “zoom” transformation to either zoom-in or zoom-out on        the particular image    -   Crop the image in one or more areas    -   Rotate the placement of certain objects in the image.    -   Adjust the photo temperature    -   Adjust the opacity of the image    -   Add a person in the image    -   Adjust certain features of the people in the image, such as hair        color, or hairstyle, facial hair, etc.    -   Adjust the clothing colors, variations, styles or items they are        wearing in the image    -   Adjust age features of the people in the images    -   Adjust facial look (feature enhancement, makeup, distortion,        gender, e.g. Snapchat filters).    -   Add a person of a certain ethnicity into the contents    -   Adjust the action or scenario of the image    -   Combine multiple elements of two or more images    -   Warp the image    -   Adjust the time of day of the image    -   Apply a gradient or pattern to the image    -   Adjust the perspective of the photo (or image layer)    -   Adjust the emotion or sentiment of a person in the image or        photo    -   Adjust the positioning or posture of a person in the image    -   Adjust the facial prominence    -   Adjust playback speed of a video or GIF    -   Change the size of a video or GIF    -   Splice a section out of a video or splice two videos together    -   Generate a highlight reel of one or more video clips or video        files    -   Make a video montage from images and/or videos    -   Choose the optimal thumbnail for a video    -   Transform the thumbnail image for a video using any of the        transforms described here    -   Transform a GIF using any of the transforms described here    -   Chang the image into a video or GIF by animating a feature    -   Changing the file format of a video or image    -   Changing a video into a GIF or vice versa    -   Make a video or GIF loop in a different way. E.g. going forward        through all frames and then reversing rather than starting back        at the beginning.    -   Adjustments to the audio content and audio files    -   Adjustments to acoustic features of a voice recording    -   Adjustments to a musical score    -   Adjustments to the tone, pitch, prosody, etc. of someone        speaking live    -   Pitch correction    -   Edit the start time and stop time of the video    -   Edit the start time and stop on the audio file    -   Edit the duration of any sound on the audio file    -   Add or adjust fades into or out of a clip, or between clips    -   Add a cross-fading effect    -   Change the audio by means of compression    -   Change the audio by means of equalization    -   Add other effects to the video, image, and/or audio files.    -   Identify the optimal take(s) from a video or audio recording        session    -   Add a flanging effect to the audio    -   Add a reverb effect to the audio    -   Adjust noise reduction effect    -   Transform a video frame, still image, or collection of frames in        a video    -   Layer an effect on top of a video    -   Apply a transformation to a specific time period in a video    -   Splice video frames into a GIF    -   Convert longer-form video contents into shorter-form contents        (e.g. truncating a video from 30 seconds to 3 seconds)

In general, the system 1000 may create a huge amount of new data relatedto the types of creative and content attributes and specificoptimizations that are effective with a target audience, or that areeffective for a particular program or campaign goal. In someimplementations, this information can be leveraged to generate dataproducts and tools in addition to the outputs of the system 1000. Forexample, if ten companies use the system 1000 to optimize theirrespective content items for campaigns and programs for sports fans, thesystem 1000 can produce a rich dataset on what works well (or does notwork well) with sports fans, according to at least one performancemetric. It should be understood that, in this example, each company maybe selling very different types of products in their programs. Forexample, a first company may sell athletic shoes, a second company maysell event tickets, a third company may sell airline and travel tickets,a fourth company may sell sporting goods, etc. Thus, the datasetproduced by the system 1000 in this example can be a highly valuabledataset based on which new transformations or combined transformationsmay be derived and offered to other users at a later time. The samedataset and analysis insights can be applied to produce a tool thatgenerates designs for new retail or sports merchandise. It canadditionally be utilized to inform marketing, advertising and customerservice initiatives tailored for that audience.

In addition, datasets such as those described in the example above mayhave applications that are useful in areas outside of marketing oradvertising. For example, transformations can be applied directly to anykind of content item including text (including characters and symbols),images, and video that are viewed on a screen or computing device. Also,such transformations could be applied to applications that are involvedin the planning, production, or refinement of physical goods orservices, or in many different areas of design. That is, the systems andmethods described herein have applications in industries and businessprocesses that involve physical and real-life design choices.

In some implementations, the system 1000 may be used to generate anaudio transformation. That is, the system 1000 could transform acandidate audio content item in an audible manner along many differentdimensions. For example, if an audio content item is targeting users whoare elderly or hard of hearing, the system 1000 may transform the audiocontent item to adjust the vocals to be higher in tone, pitch, prosody,etc. In general, any aspect of the acoustic features of the voice may besubject to transformation by the system 1000. In another example, thesystem 1000 may make adjustments to the music playing in the content, orswap in new musical elements. The system 1000 may also increase thevolume of such sonic content. This could be particularly useful in foraudio included in advertisements, training and instruction videos,entertainment, and in other video broadcasts.

For example, if the system 1000 determined that a target audience for acontent item including audio is more receptive to a narrator or voiceactor who has a female British accent, the system 1000 may be able totransform the original voice recording to reflect the desired vocaland/or musical attributes that would be optimized for maximum businessperformance. This may be achieved by swapping out the current narrationof the audio content item with pre-recorded narration or by applying oneor more transformations to an audio file corresponding to the audiocontent item and/or editing the audio file automatically.

In some implementations, the system 1000 may be used to select aparticular person (e.g. an actor, a model, etc.) to be featured in aparticular image or other content item, from among a group of people.For example, images containing one or more people may be uploaded andreceived by the system 1000. The system 1000 can then determine whichimages are optimal based upon an analysis of various attributes of thepeople in the images. These attributes may include facial or bodyattributes. Based on this analysis, the system 1000 can select a subsetof one or more of the people depicted in the images for inclusion in acandidate content item directed towards a target audience, based on alikelihood that content items including the selected subset of peopleare likely to perform well according to at least one performance metric.In an example, a user may upload a candidate content item and may selecta target audience, and the system 1000 can determine and providerecommendations for ideal characteristics of people to be depicted inthe content item. The system 1000 may recommend that a person havingparticular attributes, such as age, gender, race, ethnicity, etc.,should be included in the content item to make the content item moreappealing among the target audience. For example, the system 1000 maydetermine that including an image of a middle-aged person in the contentitem could result in better performance than including an image of ayoung person in the content item. Similarly, the system 1000 can befurther configured to select a particular middle-aged person from amonga group of middle-aged people whose photos are available, based on ananalysis of the photos indicating that a content item including an imageof the selected person is likely to perform better among the targetaudience than a content item including an image of a different person.It should be understood that the system 1000 could determine anycombination of attributes or numbers of such people to include in acontent item.

One of skill in the art will appreciate that the examples describedabove are illustrative only, and are not intended to be exhaustive ofthe types of applications that may be relevant to the system 1000. Byway of example, the following list provides a variety of other types ofapplications and technology areas that may benefit from the systems andmethods described herein.

Other Applications for the Systems and Methods Described Herein:

-   -   Virtual reality and augmented reality applications        -   Transform the contents of AR and VR applications, digital            experiences, and visual environments.    -   Gaming        -   Transform the contents of mobile, console, PC gaming, etc.        -   Transform a still image in a gaming environment.    -   Entertainment        -   Transform the contents of television programs, motion            pictures, sports games, live video broadcasts etc.    -   Ecommerce/Retail        -   Transform the presentation of products or selection of            products in an ecommerce store, or in-store display/aisle.        -   If a user's preference for the color purple is known,            content items shown to the user (e.g., via a website or a            touchscreen interface inside a store) may be displayed with            a purple treatment. Alternatively, items that are close to            the preferred purple color can be emphasized.    -   Fashion        -   Inform and transform new styles of apparel, clothing,            accessory designs, etc.        -   Transformations can be used for the purpose of informing            fabric selections and purchases, and even in textile            generation    -   Architecture and construction        -   Transform the structural design, layout, and materials            composition.        -   Transform computer-aided designs (CAD) developed by            architects and architecture professionals.        -   Transform the presentation of the building in photography            and collateral.    -   Interior design, graphic design, industrial design, app design,        web design, art direction, logo design, etc.        -   Transform visual design assets, photos, and new digital            creations to be more on-audience.    -   Product Design & product variations        -   For example, a can of beer may be transformed. In another            example, the structural design of a home appliance may be            transformed. In another example, the design of a pair of            women's tennis shoes may be transformed to inherit the            qualities of footwear that appeal to that audience.    -   Merchandising & Product packaging        -   Inform and transform the product display and packaging, and            labeling of a wide variety of consumer products from cereals            to chewing gum.    -   Travel and transportation        -   Transform the content that is being displayed on screens in            the airplane        -   Transform the locations and scenes that are displayed to            prospective travelers on a digital screen or website        -   Transform the images of travel destinations that are            displayed to users in search results, on websites, and on            other digital media.    -   Search Results (e.g. from a search engine)        -   Transform the way that webpage contents are displayed to a            particular site user. For example, the order and priority of            the URIs may change. The site descriptions themselves may            change (text optimization) depending on who the search user            is. The image content on the site or on an individual            webpage may transform as well, such as the landing page of a            website.        -   Promoted offers in search listings (e.g. Google Ads) may            transform as well.    -   Paper, plastic, boxes, etc.        -   Transformations can be applied to physical products and            printed products, e.g. patterns on cardboard boxes,            containers, posters, displays, etc.    -   Inventory management        -   Design transformations for certain products may also            integrate and inform inventory purchase decisions and            inventory management. For example, if the system 1000            determines that red sandals will appeal most to the audience            of users, then the system may recommend and/or trigger that            more red sandals should be ordered, produced, and delivered            to a particular location or website for consumption by the            target audience.        -   Similarly, the system may determine that in order to induce            more sales of red sandals, digital marketing and advertising            content items should be transformed to include color-based            transformations that emphasize shades of red. The system may            also perform a transformation to include or emphasize            sandals in the visual assets.    -   Retail and Sports merchandise        -   Transform branded merchandise materials, e.g. the look and            feel of jerseys and novelty items and the display of such            items in promotional materials.    -   Customer service        -   Transform the communications and content that are being sent            to one or more customers, either in digital or non-digital            formats.        -   Transform the experiences that customers receive in            call-centers or via online chat mechanisms.    -   Programming and computing        -   Automatically adjust or re-write code in an application            based on the audience activity, user preference or response            data. This could also inform the adjustment of a            configuration setting or inform an engineering priority            and/or task to be accomplished to optimize user experience            or application performance.    -   Printing and 3D printing        -   Transform the printing settings and design choices to            emphasize the design attributes that are preferred by a user            or target audience.    -   Automotive        -   Transform the design, manufacture and promotion of new            automobile models and the presentation of such vehicles and            accessories in promotional materials.    -   Digital billboards        -   Transform the content that is displayed to passersby in a            personalized manner according to the visual preferences of            the passersby.    -   Store displays        -   Transformations to the digital display technologies in a            retail store, e.g. text-based content items may be displayed            in a larger font when an older individual views them.            Preferred items, fonts, designs, etc. may be emphasized as            well.    -   Photography    -   Video production and motion graphics    -   Video design    -   Art direction    -   Graphic design    -   Web design    -   Interior design    -   Fashion design    -   Product design    -   Merchandising    -   Animation    -   User experience design    -   Industrial design    -   Interactive design    -   Instructional design    -   Music videos    -   Computer fine art and commercial art    -   Illustration    -   Parametric design    -   Store design and showroom design    -   Virtual store design (e.g. e-commerce store)    -   Creative writing    -   Copywriting/content writing    -   Screenwriting    -   Expository writing    -   Persuasive writing    -   Descriptive writing    -   Narrative writing    -   Dramatic writing    -   Academic writing    -   Writing literature (fiction and non-fiction)    -   Poetry    -   Comics    -   Journalistic Writing    -   Biography and autobiographical writing    -   Technical writing    -   Corporate videos    -   Webinars    -   Culture videos    -   Event videos    -   Presentation videos    -   PowerPoint videos    -   Online video advertising    -   Promotional videos    -   Testimonial videos    -   Product videos    -   Sizzle reels/Highlight reels    -   Advertising videos    -   Real estate videos    -   Training videos    -   Tip series videos    -   Explainer videos    -   Educational videos    -   Infomercials    -   Travel videos    -   Wedding videos    -   Documentary videos    -   Ceremonial videos    -   Video public service announcements (PSAs)    -   Interview videos    -   Digital journalism/ news videos    -   Music videos    -   Television commercials    -   Digital art    -   Streaming online content experiences    -   Subscription contents    -   Sales contents and materials

FIG. 11 is a flow diagram illustrating a method 1100 for transformingone or more content items in accordance with an illustrative embodiment.In some implementations, the method 1100 can be performed by a systemsuch as the system 1000 shown in FIG. 10. In brief overview, the method1100 includes receiving a plurality of harvest content items (operation1105), ranking each of the plurality of harvest content items based onat least one performance metric (operation 1110), determining matchingcriterion aspects of the plurality of harvest content items (operation1115), comparing aspects of a candidate content item with the pluralityof harvest content items according to the determined matching criterionaspects (operation 1120), determining a subset of the plurality ofharvest content items that are relevant to the candidate content itembased on the comparison of the aspects of the candidate content itemwith the plurality of harvest content items (operation 1125), andselecting a first transformation for the candidate content item(operation 1130).

Referring again to FIG. 11, the method 1100 includes receiving aplurality of harvest content items (operation 1105). In someimplementations, this operation can be performed by a content itemharvesting module such as the content item harvesting module 1030 shownin FIG. 10. The content item harvesting module can receive the contentitems from any content source, such as the content sources 1015, theaudience computing devices 1010, or the user computing device 1020 shownin FIG. 10. The content items can include text entries, images, GIFs,videos, audio data, or any other type or form of content. In someimplementations, the content item harvesting module can be configured toharvest the content items based on a particular target audience. Thetarget audience can be selected based on a preference of a user. Forexample, a user may provide one or more candidate content items to thecontent evaluation system 1005, along with an indication of a targetaudience. Thus, if the user works at a business selling sporting goods,the content item can be advertisement for sporting goods and the targetaudience can be people who are interested in one or more sports. In thisexample, the content item harvesting module can be configured to harvestcontent items that are relevant to the selected audience of peopleinterested in sports. For example, the content item harvesting modulemay harvest content items from social media accounts associated withprofessional sports teams or other businesses that market to sportsfans. The content item harvesting module also may harvest content itemsfrom social media accounts of individuals who appear to have an interestin sports.

The method 1100 also includes ranking each of the plurality of harvestcontent items based on at least one performance metric (operation 1110).In some implementations, this operation can be performed by aperformance metric ranking engine such as the performance metric rankingengine 1035 shown in FIG. 10. The ranking can be based, for example, onaudience measurement data that may be collected by an performancemeasurement module such as the performance measurement module 1065 shownin FIG. 10. In some implementations, the performance metric rankingengine can be configured to generate a numerical score for each harvestcontent item that represents the performance of that content item amongthe target audience for a given performance metric. Examples of suchperformance metrics are provided above.

The method 1100 also includes determining matching criterion aspects ofthe plurality of harvest content items (operation 1115). In someimplementations, this operation can be performed by a matching criterionmanager such as the matching criterion manager 1040 shown in FIG. 10. Ingeneral, the matching criterion aspects can be any aspect,characteristic, or category of the content items, such as thosedescribed above. For example, for image-based content items, matchingcriterion aspects may include colors included within the images, objectsor people included within the images, filters applied to the images, orany other visual aspects of the images. In some implementations, amatching criterion aspect may include a category of one or more contentitems. For example, category information can be stored in electroniccontent items as metadata, or may instead be inferred based on othercharacteristics of the content item. There is no limit to the number ofmatching criterion aspects that can be selected by the matchingcriterion manager. The matching criterion manager can also be configuredto compare aspects of a candidate content item with the plurality ofharvest content items according to the determined matching criterionaspects (operation 1120). As described above, the candidate content itemcan be received from a user (e.g., from the user computing device 1020shown in FIG. 10), and may generally correspond to any content item thatthe user is considering publishing and would like to have evaluated ortransformed prior to publishing. The matching criterion manager cancompare any or all of the identified matching criterion aspects of eachof the harvest content items with corresponding aspects of the candidatecontent item in order to determine harvest content items that aresimilar to the candidate content item. The matching criterion managercan similarly determine harvest content items that are relevant for aparticular candidate transformation, or for a performance evaluation ofa new content item or campaign.

The method 1100 includes determining a subset of the plurality ofharvest content items that are relevant to the candidate content itembased on the comparison of the aspects of the candidate content itemwith the plurality of harvest content items (operation 1125). In someimplementations, this operation also can be performed by the matchingcriterion manager. In some implementations, the subset of the pluralityof harvest content items can include those harvest content items havingthe highest number of matching criterion aspects that are similar tocorresponding aspects of the candidate content item. It should beunderstood that there is no limit on the number of harvest content itemsthat can be included in the identified subset of harvest content items.In some implementations, each pair of content items including thecandidate content item and one of the harvest content items can beassigned a numerical score representing a degree of similarity betweenthe two content items, and the identified subset of harvest contentitems can include all of the harvest content items whose correspondingpair has a score exceeding a predetermined threshold. In otherimplementations, the subset of harvest content items can be determinedin a different manner. For example, the subset of harvest content itemsmay include content items that are not necessarily most similar orrelevant to the candidate content item, but are most relevant to thecandidate content item according to a different metric. In otherimplementations, the candidate content item may be determined to berelevant to all harvest content items, rather than to a subset ofcontent items.

The method 1100 also includes selecting a first transformation for thecandidate content item (operation 1130). In some implementations, thisoperation can be performed by a recommendation module such as therecommendation module 1045 shown in FIG. 10. The first transformationcan be any of the transformations described above. In someimplementations, the first transformation is selected to make at leastone characteristic of the candidate content item more like the at leastone characteristic of a first harvest content item of the subset of theplurality of harvest content items that is ranked more highly than asecond harvest content item of the subset of the plurality of harvestcontent items. Thus, the transformation can be expected to result in atransformed content item that also is likely to be ranked more highlythan the original candidate content item. The characteristic can relateto any quality of the content item that can be perceived by an audience.For example, the characteristic can be any visual characteristic (e.g.,if the content item includes image or video data) or any auditorycharacteristic (e.g., if the content item includes audio). Thetransformation can be any alteration of any portion of the content itemthat results in a change in the identified characteristic.

In some implementations, the method 1100 also can include applying theselected transformation to the candidate content item to generate thetransformed content item. For example, the transformation can be appliedby a content transformation module such as the content transformationmodule 1050 shown in FIG. 10. In some implementations, the contenttransformation module can be further configured to select an intensityof the transformation to be applied. For example, if the transformationto be applied is determined to be a red treatment applied to animage-based candidate content item, the content transformation modulecan be configured to select a degree to which the red treatment shouldbe applied. This can include determining color parameters such as hueand saturation level to be applied across the candidate content item, orto particular locations, or layers, within the candidate content item.It should be understood that the term “intensity” can refer to differentcharacteristics depending on the selected transformation. Generally, theterm intensity can relate to a degree to which the content item isaltered from its original state by application of the selectedtransformation. Thus, a higher intensity can result in a content itemthat has been altered more heavily relative to the alteration applied toa content item whose transformation intensity is relatively lower.

In some implementations, the method 1100 also can include calculating ascore for the candidate content item. For example, such a score can becalculated by a performance metric predictor such as the performancemetric predictor 1060 shown in FIG. 10. The score can be calculatedbased on a performance metric and a target audience. In someimplementations, the user that provides the candidate content item foranalysis and transformation also can provide an indication of theperformance metric and target audience to be used in the calculation ofthis score. Generally, the score may relate to a likelihood that thecandidate content item will perform well with the target audience inrelation to the selected performance metric. In some implementations,the recommendation module can be configured to select the firsttransformation (i.e., operation 1130 of the method 1100) such that, uponapplication of the first transformation to the candidate content item togenerate a first transformed content item, a score of the firsttransformed content item is improved, or is likely to improve, relativeto the score of the candidate content item.

In some implementations, the performance metric predictor can beconfigured to calculate the score based on a variety of factors orattributes of the content item or of features in the content item. Forexample, the performance metric predictor can calculate the score basedon any combination of the following attributes: layout and composition,position, symmetry, balance, arrangement (e.g. golden mean, rule ofthirds), object location, depth of field, angle of view, focal point,view point, vantage point, foreground/background content, whitespace/negative space, cropping, framing, color scheme, hue, tint,temperature, tone, contrast, saturation, brightness, shade, mood, line,angles, noise, contours, gradients, texture, repetition, patterns,blowout, blooming, concentricity, cubic attributes, geometricattributes, shadow, blocked shadow, vignetting, objects, scale, numberof objects, position of objects, spatial context, proportion, shapes,shape of objects, number of shapes, attributes of objects, form,perspective, representation, path, scenery, time of day, exposure, timelapse, typography, position of headline, size of headline, length oftext, location of call-to-action, typeface, font, location of faces,posture/pose of people, location of figures, gestures, action/activitiesof people, number of people, hair color of people, ethnicity of people,gender of people, age of people, expressions and emotions of people,facial attributes, clothing and appearance, accessories, resolution,orientation, icons, emojis, logos, watermarks, etc. It should beunderstood that this list of attributes is exemplary only, and should benot read as limiting the scope of this disclosure.

It should be understood that, in some implementations, therecommendation module may not be limited to recommending transformationsfor a candidate content item. For example, in some otherimplementations, the recommendation module can be configured torecommend an alternate candidate content item, if the user provides morethan one content item for evaluation, or if the system has access to aplurality of other content items. The user may not have provided theseother content items. Thus, in some implementations the method 1100 caninclude calculating a score each of two or more candidate content items,and the recommendation module can recommend choosing, selecting,prioritizing, improving, or publishing the candidate content item havingthe highest score, without applying any transformation to the candidatecontent items.

Furthermore, it should be noted that the recommendation module mayrecommend more than one type of transformation for a single candidatecontent item. For example, in some implementations the method 1100 caninclude determining two or more different transformations, either orboth of which may be applied to a single candidate content item toimprove the score of the candidate content item. In someimplementations, the recommendation module can provide an indication ofthe two or more different transformations to the user who provided thecandidate content item. In some other implementations, the indicationcan be provided to the user by a user alert module such as the useralert module 1070 shown in FIG. 10. As discussed above, such anindication can be provided via a GUI that may be implemented as abrowser extension, a standalone application, or through other means.Such a GUI may be generated, for example, by a GUI generation modulesuch as the GUI generation module 1075 shown in FIG. 10. The GUI alsocan allow the user to provide feedback to the content evaluation system.Thus, in some implementations, the method 1100 can further includereceiving a user input corresponding to a selection of at least one ofthe two or more transformations recommended to the user. The contenttransformation module can then apply the selected one or moretransformations to the candidate content item, and can return thetransformed content item to the user. The user feedback may also becaptured after a transformation has been applied to one or morecandidate content items. User feedback may also be captured after aparticular advertising campaign has launched or concluded.

In some implementations, after a content item (which may be an originalcandidate content item or a transformed content item) has beenpublished, the method 1100 can further include determining a response ofthe target audience to the content item. In some implementations, thisoperation can be performed by audience performance measurement modulesuch as the performance measurement module 1065 shown in FIG. 10. Theresponse can include any type and form or interaction or response fromthe target audience, such as a number of clicks, a total or averageduration of time during which members of the target audience viewed orotherwise interacted with the content item, sales that result fromaudience interaction with the content item, sales performance during thetime period of the campaign, week-to-week sales performance from channelpartners and retailers, household penetration figures, etc. In someimplementations, the recommendation module also can be configured torecommend at least a second target audience for the published contentitem. For example, the content evaluation system can be configured todetermine a second audience among which the content item is likely toperform well according to at least one performance metric, and therecommendation module can generate a recommendation corresponding tothis second target audience. The recommendation can then be provided tothe user. In this way, the system can inform the user of new targetaudiences who that the user may not have considered, and which arelikely to respond well to the user's content items.

In some implementations, the method 1100 can further include determiningan updated score for a published content item (e.g., either an originalcandidate content item or a transformed content item) based on theresponse of the target audience to the published content item. Thus, theupdated score may indicate that the audience has become either more orless receptive to the content item over time. This operation can beperformed, for example, by the performance metric predictor. In someimplementations, the performance metric predictor also can be configuredto select an additional transformation for the content item based on theupdated score. For example, the additional transformation can beselected such that, upon application of the additional transformation tothe content item by the content transformation module, a score of thecontent item is improved relative to the updated score of the contentitem in its previous state. The user alert module or the recommendationmodule can be configured to provide a message to the user including arecommendation to apply the additional transformation to the contentitem.

FIG. 12 is a flow diagram illustrating a method 1200 for evaluating oneor more content items in accordance with an illustrative embodiment. Insome implementations, the method 1200 can be performed by a system suchas the system 1000 shown in FIG. 10. In brief overview, the method 1200includes receiving a plurality of harvest content items (operation1205), ranking each of the plurality of harvest content items based onat least one performance metric (operation 1210), determining matchingcriterion aspects of the plurality of harvest content items (operation1215), comparing aspects of a first candidate content item with theplurality of harvest content items according to the determined matchingcriterion aspects (operation 1220), determining a subset of theplurality of harvest content items that are relevant to the firstcandidate content item based on the comparison of the aspects of thecandidate content item with the plurality of harvest content items(operation 1225), calculating a score for the first candidate contentitem (operation 1130), identifying at least one characteristic of thefirst candidate content item (operation 1235), and determining whetherthe identified characteristic of the first candidate content item isassociated with a likely increase or a decrease in the score for thefirst candidate content item (operation 1240).

Referring again to FIG. 12, the operations 1205, 1210, 1215, 1220, and1225 are similar to operations 1105, 1110, 1115, 1120, and 1125,respectively, of the method 1100 described above, and can be performedin a similar manner. The method 1200 also includes calculating a scorefor the first candidate content item (operation 1130). In someimplementations, this step is carried out by a performance metricpredictor such as the performance metric predictor 1060 shown in FIG.10. The score can be calculated based on at the least one performancemetric and at least one target audience. As described above, the userthat provides the first candidate content item for analysis also canprovide an indication of the performance metric and the target audienceto be used in the calculation of this score. Generally, the score mayrelate to a likelihood that the candidate content item will perform wellwith the target audience in relation to the selected performance metric.It may also be inferred that more highly scored content items increasethe likelihood of higher performing campaigns.

The method 1200 also includes identifying at least one characteristic ofthe first candidate content item (operation 1235) and determiningwhether the identified characteristic of the first candidate contentitem is associated with an increase or a decrease in the score for thefirst candidate content item (operation 1240). In some implementations,these operations can be performed by a recommendation module such as therecommendation module 1045 shown in FIG. 10. The characteristic can beany type or form of characteristic of the first candidate content item,including any visual, text, or auditory characteristic. In someimplementations, the method 1200 can further include providing a messageto the user including information corresponding to the determination ofwhether the identified characteristic of the first candidate contentitem is associated with an increase of a decrease in the score for thefirst candidate content item. Thus, the user can be alerted to the factthat the characteristic is predicted to be beneficial or harmful to theperformance of the first candidate content item among the targetaudience. Low performing or low-value characteristics may also beomitted from the set of transformed contents that are presented to theuser.

In some implementations, the method 1200 can further include receivingat least a second candidate content item from the user, calculating ascore for the second candidate content item, and ranking the firstcandidate content item and the second candidate content item accordingto their respective scores. The scoring and ranking of the secondcandidate content items can be performed, for example, by theperformance metric predictor in a manner similar to that used forscoring the first candidate content item. The ranking can indicate whichof the first candidate content item and the second candidate contentitem is likely to perform better with the target audience based on theperformance metric. In some implementations, the alert module can alsobe configured to provide a second message to the user includinginformation corresponding to the ranking of the first candidate contentitem and the second candidate content item.

In an illustrative embodiment, any of the operations described hereincan be implemented at least in part as computer-readable instructionsstored on a computer-readable medium or memory. Upon execution of thecomputer-readable instructions by a processor, the computer-readableinstructions can cause a computing device to perform the operations.

The foregoing description of illustrative embodiments has been presentedfor purposes of illustration and of description. It is not intended tobe exhaustive or limiting with respect to the precise form disclosed,and modifications and variations are possible in light of the aboveteachings or may be acquired from practice of the disclosed embodiments.It is intended that the scope of the invention be defined by the claimsappended hereto and their equivalents.

What is claimed is:
 1. A system for automatically transforming a contentitem, the system comprising a processor having programmed instructionsto: receive a plurality of harvest content items; rank each of theplurality of harvest content items based on at least one performancemetric; determine matching criterion aspects of the plurality of harvestcontent items; compare aspects of a candidate content item with theplurality of harvest content items according to the determined matchingcriterion aspects; determine a subset of the plurality of harvestcontent items that are relevant to the candidate content item based onthe comparison of the aspects of the candidate content item with theplurality of harvest content items; select a first transformation forthe candidate content item to make at least one characteristic of thecandidate content item more like the at least one characteristic of afirst harvest content item of the subset of the plurality of harvestcontent items that is ranked more highly than a second harvest contentitem of the subset of the plurality of harvest content items; determinean intensity associated with the selected transformation prior toapplying the selected transformation to the candidate content item; andapply the selected first transformation to the candidate content item togenerate a transformed content item.
 2. The system of claim 1, wherein:the candidate content item comprises an image including a plurality oflayers; and wherein the processor is further programed with instructionsto apply the selected transformation by altering a subset of theplurality of layers.
 3. The system of claim 1, wherein: the candidatecontent item comprises a video including a plurality of frames; andwherein the processor is further programed with instructions to applythe selected transformation by altering a subset of the plurality offrames.
 4. The system of claim 1, wherein the processor is furtherprogramed with instructions to calculate a score for the candidatecontent item, based on the at least one performance metric and at leastone target audience.
 5. The system of claim 4, wherein the at least oneperformance metric comprises at least one of a uniqueness performancemetric and an engagement metric.
 6. The system of claim 4, wherein theprocessor is further programed with instructions to calculate a secondscore for the candidate content item, based on at least a second targetaudience.
 7. The system of claim 4, wherein the processor is furtherprogramed with instructions to select the first transformation suchthat, upon application of the first transformation to the candidatecontent item to generate a first transformed content item, a score ofthe first transformed content item is improved relative to the score ofthe candidate content item.
 8. The system of claim 4, wherein theprocessor is further programed with instructions to generate arecommendation of an alternate content item having a score that ishigher than the score of the candidate content item.
 9. The system ofclaim 4, wherein the processor is further programed with instructions todetermine a response of the target audience to the first transformedcontent item, subsequent to the first transformed content item beingpublished.
 10. The system of claim 9, wherein the processor is furtherprogramed with instructions to generate a recommendation of a secondtarget audience for the transformed content item, the second targetaudience different from the first target audience.
 11. The system ofclaim 9, wherein: wherein the processor is further programed withinstructions to determine an updated score for the first transformedcontent item, based on the determined response of the target audience tothe first transformed content item; and wherein the processor is furtherprogramed with instructions to select a second transformation such that,upon application of the second transformation to the candidate contentitem by the content transformation module to generate a secondtransformed content item, a score of the second transformed content itemis improved relative to the updated score of the first transformedcontent item.
 12. The system of claim 11, wherein the processor isfurther programed with instructions to select the second transformationbased at least in part on preference information received from a userassociated with the candidate content item.
 13. The system of claim 9,wherein the processor is further programed with instructions to providea message to a user associated with the candidate content item, themessage including a recommendation to apply the second transformation tothe candidate content item.
 14. The system of claim 1, wherein theprocessor is further programed with instructions to select a secondtransformation for the candidate content item, the second transformationdifferent from the first transformation.
 15. The system of claim 14,wherein the processor is further programed with instructions to provide,to a user associated with the candidate content item, an indication ofthe first transformation and the second transformation.
 16. The systemof claim 15, wherein the processor is further programed withinstructions to provide a GUI for display on a computing device of theuser, wherein the indication of the first transformation and the secondtransformation is provided via the GUI displayed on the user computingdevice.
 17. The system of claim 16, wherein the processor is furtherprogramed with instructions to provide the GUI via at least one of anextension of a web browser executing on the user computing device and aclient application executing on the client computing device.
 18. Thesystem of claim 15, wherein the processor is further programed withinstructions to: receive, from the user, a user input corresponding to aselection of at least one of the first transformation and the secondtransformation; apply at least one of the first transformation and thesecond transformation to the candidate content item to generate atransformed content item, based on the user input; and return thetransformed content item to the user.
 19. The system of claim 1, whereinthe processor is further programed with instructions to receive theplurality of harvest content items based on at least one targetaudience.
 20. The system of claim 1, wherein at least one of thematching criterion aspects comprises a content category.
 21. A methodfor automatically transforming a content item, the method comprising:receiving, by a content item harvesting module, a plurality of harvestcontent items; ranking, by a performance metric ranking engine, each ofthe plurality of harvest content items based on at least one performancemetric; determining, by a matching criterion manager, matching criterionaspects of the plurality of harvest content items; comparing, by thematching criterion manager, aspects of a candidate content item with theplurality of harvest content items according to the determined matchingcriterion aspects; determining, by the matching criterion manager, asubset of the plurality of harvest content items that are relevant tothe candidate content item based on the comparison of the aspects of thecandidate content item with the plurality of harvest content items;selecting, by a recommendation module, a first transformation for thecandidate content item to make at least one characteristic of thecandidate content item more like the at least one characteristic of afirst harvest content item of the subset of the plurality of harvestcontent items that is ranked more highly than a second harvest contentitem of the subset of the plurality of harvest content items;determining, by a content transformation module, an intensity associatedwith the selected transformation prior to applying the selectedtransformation to the candidate content item; and applying, by thecontent transformation module, the selected first transformation to thecandidate content item to generate a transformed content item.